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Ghanegolmohammadi F, Eslami M, Ohya Y. Systematic data analysis pipeline for quantitative morphological cell phenotyping. Comput Struct Biotechnol J 2024; 23:2949-2962. [PMID: 39104709 PMCID: PMC11298594 DOI: 10.1016/j.csbj.2024.07.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2024] [Revised: 07/09/2024] [Accepted: 07/10/2024] [Indexed: 08/07/2024] Open
Abstract
Quantitative morphological phenotyping (QMP) is an image-based method used to capture morphological features at both the cellular and population level. Its interdisciplinary nature, spanning from data collection to result analysis and interpretation, can lead to uncertainties, particularly among those new to this actively growing field. High analytical specificity for a typical QMP is achieved through sophisticated approaches that can leverage subtle cellular morphological changes. Here, we outline a systematic workflow to refine the QMP methodology. For a practical review, we describe the main steps of a typical QMP; in each step, we discuss the available methods, their applications, advantages, and disadvantages, along with the R functions and packages for easy implementation. This review does not cover theoretical backgrounds, but provides several references for interested researchers. It aims to broaden the horizons for future phenome studies and demonstrate how to exploit years of endeavors to achieve more with less.
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Affiliation(s)
- Farzan Ghanegolmohammadi
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan
| | - Mohammad Eslami
- Harvard Ophthalmology AI Lab, Schepen’s Eye Research Institute of Massachusetts Eye and Ear Infirmary, Harvard Medical School, Boston, USA
| | - Yoshikazu Ohya
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan
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2
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Lakshmanan M, Chia S, Pang KT, Sim LC, Teo G, Mak SY, Chen S, Lim HL, Lee AP, Bin Mahfut F, Ng SK, Yang Y, Soh A, Tan AHM, Choo A, Ho YS, Nguyen-Khuong T, Walsh I. Antibody glycan quality predicted from CHO cell culture media markers and machine learning. Comput Struct Biotechnol J 2024; 23:2497-2506. [PMID: 38966680 PMCID: PMC11222931 DOI: 10.1016/j.csbj.2024.05.046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 05/22/2024] [Accepted: 05/28/2024] [Indexed: 07/06/2024] Open
Abstract
N-glycosylation can have a profound effect on the quality of mAb therapeutics. In biomanufacturing, one of the ways to influence N-glycosylation patterns is by altering the media used to grow mAb cell expression systems. Here, we explore the potential of machine learning (ML) to forecast the abundances of N-glycan types based on variables related to the growth media. The ML models exploit a dataset consisting of detailed glycomic characterisation of Anti-HER fed-batch bioreactor cell cultures measured daily under 12 different culture conditions, such as changes in levels of dissolved oxygen, pH, temperature, and the use of two different commercially available media. By performing spent media quantitation and subsequent calculation of pseudo cell consumption rates (termed media markers) as inputs to the ML model, we were able to demonstrate a small subset of media markers (18 selected out of 167 mass spectrometry peaks) in a Chinese Hamster Ovary (CHO) cell cultures are important to model N-glycan relative abundances (Regression - correlations between 0.80-0.92; Classification - AUC between 75.0-97.2). The performances suggest the ML models can infer N-glycan critical quality attributes from extracellular media as a proxy. Given its accuracy, we envisage its potential applications in biomaufactucuring, especially in areas of process development, downstream and upstream bioprocessing.
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Affiliation(s)
- Meiyappan Lakshmanan
- Bioprocessing Technology Institute (BTI), Agency for Science, Technology and Research (A⁎STAR), 20 Biopolis Way, #06–01 Centros, Singapore 138668, Republic of Singapore
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, India
- Centre for Integrative Biology and Systems medicinE (IBSE), Indian Institute of Technology Madras, India
- Robert Bosch Centre for Data Science and AI (RBCDSAI), Indian Institute of Technology Madras, India
| | - Sean Chia
- Bioprocessing Technology Institute (BTI), Agency for Science, Technology and Research (A⁎STAR), 20 Biopolis Way, #06–01 Centros, Singapore 138668, Republic of Singapore
| | - Kuin Tian Pang
- Bioprocessing Technology Institute (BTI), Agency for Science, Technology and Research (A⁎STAR), 20 Biopolis Way, #06–01 Centros, Singapore 138668, Republic of Singapore
| | - Lyn Chiin Sim
- Bioprocessing Technology Institute (BTI), Agency for Science, Technology and Research (A⁎STAR), 20 Biopolis Way, #06–01 Centros, Singapore 138668, Republic of Singapore
| | - Gavin Teo
- Bioprocessing Technology Institute (BTI), Agency for Science, Technology and Research (A⁎STAR), 20 Biopolis Way, #06–01 Centros, Singapore 138668, Republic of Singapore
| | - Shi Ya Mak
- Bioprocessing Technology Institute (BTI), Agency for Science, Technology and Research (A⁎STAR), 20 Biopolis Way, #06–01 Centros, Singapore 138668, Republic of Singapore
| | - Shuwen Chen
- Bioprocessing Technology Institute (BTI), Agency for Science, Technology and Research (A⁎STAR), 20 Biopolis Way, #06–01 Centros, Singapore 138668, Republic of Singapore
| | - Hsueh Lee Lim
- Bioprocessing Technology Institute (BTI), Agency for Science, Technology and Research (A⁎STAR), 20 Biopolis Way, #06–01 Centros, Singapore 138668, Republic of Singapore
| | - Alison P. Lee
- Bioprocessing Technology Institute (BTI), Agency for Science, Technology and Research (A⁎STAR), 20 Biopolis Way, #06–01 Centros, Singapore 138668, Republic of Singapore
| | - Farouq Bin Mahfut
- Bioprocessing Technology Institute (BTI), Agency for Science, Technology and Research (A⁎STAR), 20 Biopolis Way, #06–01 Centros, Singapore 138668, Republic of Singapore
| | - Say Kong Ng
- Bioprocessing Technology Institute (BTI), Agency for Science, Technology and Research (A⁎STAR), 20 Biopolis Way, #06–01 Centros, Singapore 138668, Republic of Singapore
| | - Yuansheng Yang
- Bioprocessing Technology Institute (BTI), Agency for Science, Technology and Research (A⁎STAR), 20 Biopolis Way, #06–01 Centros, Singapore 138668, Republic of Singapore
| | - Annie Soh
- Bioprocessing Technology Institute (BTI), Agency for Science, Technology and Research (A⁎STAR), 20 Biopolis Way, #06–01 Centros, Singapore 138668, Republic of Singapore
| | - Andy Hee-Meng Tan
- Bioprocessing Technology Institute (BTI), Agency for Science, Technology and Research (A⁎STAR), 20 Biopolis Way, #06–01 Centros, Singapore 138668, Republic of Singapore
| | - Andre Choo
- Bioprocessing Technology Institute (BTI), Agency for Science, Technology and Research (A⁎STAR), 20 Biopolis Way, #06–01 Centros, Singapore 138668, Republic of Singapore
| | - Ying Swan Ho
- Bioprocessing Technology Institute (BTI), Agency for Science, Technology and Research (A⁎STAR), 20 Biopolis Way, #06–01 Centros, Singapore 138668, Republic of Singapore
| | - Terry Nguyen-Khuong
- Bioprocessing Technology Institute (BTI), Agency for Science, Technology and Research (A⁎STAR), 20 Biopolis Way, #06–01 Centros, Singapore 138668, Republic of Singapore
| | - Ian Walsh
- Bioprocessing Technology Institute (BTI), Agency for Science, Technology and Research (A⁎STAR), 20 Biopolis Way, #06–01 Centros, Singapore 138668, Republic of Singapore
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Patrício A, Costa RS, Henriques R. Pattern-centric transformation of omics data grounded on discriminative gene associations aids predictive tasks in TCGA while ensuring interpretability. Biotechnol Bioeng 2024; 121:2881-2892. [PMID: 38859573 DOI: 10.1002/bit.28758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 02/07/2024] [Accepted: 05/18/2024] [Indexed: 06/12/2024]
Abstract
The increasing prevalence of omics data sources is pushing the study of regulatory mechanisms underlying complex diseases such as cancer. However, the vast quantities of molecular features produced and the inherent interplay between them lead to a level of complexity that hampers both descriptive and predictive tasks, requiring custom-built algorithms that can extract relevant information from these sources of data. We propose a transformation that moves data centered on molecules (e.g., transcripts and proteins) to a new data space focused on putative regulatory modules given by statistically relevant co-expression patterns. To this end, the proposed transformation extracts patterns from the data through biclustering and uses them to create new variables with guarantees of interpretability and discriminative power. The transformation is shown to achieve dimensionality reductions of up to 99% and increase predictive performance of various classifiers across multiple omics layers. Results suggest that omics data transformations from gene-centric to pattern-centric data supports both prediction tasks and human interpretation, notably contributing to precision medicine applications.
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Affiliation(s)
- André Patrício
- INESC-ID and Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
- LAQV-REQUIMTE, Department of Chemistry, NOVA School of Science and Technology, NOVA University Lisbon, Caparica, Portugal
| | - Rafael S Costa
- LAQV-REQUIMTE, Department of Chemistry, NOVA School of Science and Technology, NOVA University Lisbon, Caparica, Portugal
| | - Rui Henriques
- INESC-ID and Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
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Borah K, Das HS, Seth S, Mallick K, Rahaman Z, Mallik S. A review on advancements in feature selection and feature extraction for high-dimensional NGS data analysis. Funct Integr Genomics 2024; 24:139. [PMID: 39158621 DOI: 10.1007/s10142-024-01415-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2024] [Revised: 07/30/2024] [Accepted: 08/01/2024] [Indexed: 08/20/2024]
Abstract
Recent advancements in biomedical technologies and the proliferation of high-dimensional Next Generation Sequencing (NGS) datasets have led to significant growth in the bulk and density of data. The NGS high-dimensional data, characterized by a large number of genomics, transcriptomics, proteomics, and metagenomics features relative to the number of biological samples, presents significant challenges for reducing feature dimensionality. The high dimensionality of NGS data poses significant challenges for data analysis, including increased computational burden, potential overfitting, and difficulty in interpreting results. Feature selection and feature extraction are two pivotal techniques employed to address these challenges by reducing the dimensionality of the data, thereby enhancing model performance, interpretability, and computational efficiency. Feature selection and feature extraction can be categorized into statistical and machine learning methods. The present study conducts a comprehensive and comparative review of various statistical, machine learning, and deep learning-based feature selection and extraction techniques specifically tailored for NGS and microarray data interpretation of humankind. A thorough literature search was performed to gather information on these techniques, focusing on array-based and NGS data analysis. Various techniques, including deep learning architectures, machine learning algorithms, and statistical methods, have been explored for microarray, bulk RNA-Seq, and single-cell, single-cell RNA-Seq (scRNA-Seq) technology-based datasets surveyed here. The study provides an overview of these techniques, highlighting their applications, advantages, and limitations in the context of high-dimensional NGS data. This review provides better insights for readers to apply feature selection and feature extraction techniques to enhance the performance of predictive models, uncover underlying biological patterns, and gain deeper insights into massive and complex NGS and microarray data.
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Affiliation(s)
- Kasmika Borah
- Department of Computer Science and Information Technology, Cotton University, Panbazar, Guwahati, 781001, Assam, India
| | - Himanish Shekhar Das
- Department of Computer Science and Information Technology, Cotton University, Panbazar, Guwahati, 781001, Assam, India.
| | - Soumita Seth
- Department of Computer Science and Engineering, Future Institute of Engineering and Management, Narendrapur, Kolkata, 700150, West Bengal, India
| | - Koushik Mallick
- Department of Computer Science and Engineering, RCC Institute of Information Technology, Canal S Rd, Beleghata, Kolkata, 700015, West Bengal, India
| | | | - Saurav Mallik
- Department of Environmental Health, Harvard T H Chan School of Public Health, Boston, MA, 02115, USA.
- Department of Pharmacology & Toxicology, University of Arizona, Tucson, AZ, 85721, USA.
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Sassi M, Carnevale A, Mancuso M, Schena E, Pecchia L, Longo UG. Machine-learning models for shoulder rehabilitation exercises classification using a wearable system. Knee Surg Sports Traumatol Arthrosc 2024. [PMID: 39154254 DOI: 10.1002/ksa.12431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Revised: 07/26/2024] [Accepted: 07/27/2024] [Indexed: 08/19/2024]
Abstract
PURPOSE The objective of this study is to train and test machine-learning (ML) models to automatically classify shoulder rehabilitation exercises. METHODS The cohort included both healthy and patients with rotator-cuff (RC) tears. All participants performed six shoulder rehabilitation exercises, following guidelines developed by the American Society of Shoulder and Elbow Therapists. Each exercise was repeated six times, while wearing a wearable system equipped with three magneto-inertial sensors. Six supervised machine-learning models (k-Nearest Neighbours, Support Vector Machine, Decision Tree, Random Forest (RF), Logistic Regression and Adaptive Boosting) were trained for the classification. The algorithms' ability to accurately classify exercise activities was evaluated using the nested cross-validation method, with different combinations of outer and inner folds. RESULTS A total of 19 healthy subjects and 17 patients with complete RC tears were enroled in the study. The highest classification performances were achieved by the RF classifier, with an accuracy of 89.91% and an F1-score of 89.89%. CONCLUSION The results of this study highlight the feasibility and effectiveness of using wearable sensors and ML algorithms to accurately classify shoulder rehabilitation exercises. These findings suggest promising prospects for implementing the proposed wearable system in remote home-based monitoring scenarios. The ease of setup and modularity of the system reduce user burden enabling patient-driven sensor positioning. LEVEL OF EVIDENCE Level III.
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Affiliation(s)
- Martina Sassi
- Fondazione Policlinico Universitario Campus Bio-Medico di Roma, Rome, Italy
- Department of Engineering, Unit of Intelligent Health Technologies, Sustainable Design Management and Assessment, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Arianna Carnevale
- Fondazione Policlinico Universitario Campus Bio-Medico di Roma, Rome, Italy
| | - Matilde Mancuso
- Fondazione Policlinico Universitario Campus Bio-Medico di Roma, Rome, Italy
| | - Emiliano Schena
- Laboratory of Measurement and Biomedical Instrumentation, Department of Engineering, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Leandro Pecchia
- Fondazione Policlinico Universitario Campus Bio-Medico di Roma, Rome, Italy
- Department of Engineering, Unit of Intelligent Health Technologies, Sustainable Design Management and Assessment, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Umile Giuseppe Longo
- Fondazione Policlinico Universitario Campus Bio-Medico di Roma, Rome, Italy
- Research Unit of Orthopaedic and Trauma Surgery, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Rome, Italy
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Zinati Z, Nazari L, Niazi A. Uncovering waterlogging-responsive genes in cucumber through machine learning and differential gene correlation analysis. BOTANICAL STUDIES 2024; 65:25. [PMID: 39141059 PMCID: PMC11324642 DOI: 10.1186/s40529-024-00433-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2024] [Accepted: 08/05/2024] [Indexed: 08/15/2024]
Abstract
As climate change intensifies, the frequency and severity of waterlogging are expected to increase, necessitating a deeper understanding of the cucumber response to this stress. In this study, three public RNA-seq datasets (PRJNA799460, PRJNA844418, and PRJNA678740) comprising 36 samples were analyzed. Various feature selection algorithms including Uncertainty, Relief, SVM (Support Vector Machine), Correlation, and logistic least absolute shrinkage, and selection operator (LASSO) were performed to identify the most significant genes related to the waterlogging stress response. These feature selection techniques, which have different characteristics, were used to reduce the complexity of the data and thereby identify the most significant genes related to the waterlogging stress response. Uncertainty, Relief, SVM, Correlation, and LASSO identified 4, 4, 10, 21, and 13 genes, respectively. Differential gene correlation analysis (DGCA) focusing on the 36 selected genes identified changes in correlation patterns between the selected genes under waterlogged versus control conditions, providing deeper insights into the regulatory networks and interactions among the selected genes. DGCA revealed significant changes in the correlation of 13 genes between control and waterlogging conditions. Finally, we validated 13 genes using the Random Forest (RF) classifier, which achieved 100% accuracy and a 1.0 Area Under the Curve (AUC) score. The SHapley Additive exPlanations (SHAP) values clearly showed the significant impact of LOC101209599, LOC101217277, and LOC101216320 on the model's predictive power. In addition, we employed the Boruta as a wrapper feature selection method to further validate our gene selection strategy. Eight of the 13 genes were common across the four feature weighting algorithms, LASSO, DGCA, and Boruta, underscoring the robustness and reliability of our gene selection strategy. Notably, the genes LOC101209599, LOC101217277, and LOC101216320 were among genes identified by multiple feature selection methods from different categories (filtering, wrapper, and embedded). Pathways associated with these specific genes play a pivotal role in regulating stress tolerance, root development, nutrient absorption, sugar metabolism, gene expression, protein degradation, and calcium signaling. These intricate regulatory mechanisms are crucial for cucumbers to adapt effectively to waterlogging conditions. These findings provide valuable insights for uncovering targets in breeding new cucumber varieties with enhanced stress tolerance.
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Affiliation(s)
- Zahra Zinati
- Department of Agroecology, College of Agriculture and Natural Resources of Darab, Shiraz University, Shiraz, Iran
| | - Leyla Nazari
- Crop and Horticultural Science Research Department, Fars Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Shiraz, Iran.
| | - Ali Niazi
- Institute of Biotechnology, School of Agriculture, Shiraz University, Shiraz, Iran.
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Schumann P, Trentzsch K, Stölzer-Hutsch H, Jochim T, Scholz M, Malberg H, Ziemssen T. Using machine learning algorithms to detect fear of falling in people with multiple sclerosis in standardized gait analysis. Mult Scler Relat Disord 2024; 88:105721. [PMID: 38885599 DOI: 10.1016/j.msard.2024.105721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 06/04/2024] [Accepted: 06/10/2024] [Indexed: 06/20/2024]
Abstract
INTRODUCTION Multiple sclerosis (MS) is the most common chronic inflammatory disease of the central nervous system. The progressive impairment of gait is one of the most important pathognomic symptoms which are associated with falls and fear of falling (FOF) in people with MS (pwMS). 60 % of pwMS show a FOF, which leads to restrictions in mobility as well as physical activity and reduces the quality of life in general. Therefore, early detection of FOF is crucial because it enables early implementation of rehabilitation strategies as well as clinical decision-making to reduce progression. Qualitative and quantitative evaluation of gait pattern is an essential aspect of disease assessment and can provide valuable insights for personalized treatment decisions in pwMS. Our objective was to identify the most appropriate clinical gait analysis methods to identify FOF in pwMS and to detect the optimal machine learning (ML) algorithms to predict FOF using the complex multidimensional data from gait analysis. METHODS Data of 1240 pwMS was recorded at the MS Centre of the University Hospital Dresden between November 2020 and September 2021. Patients performed a multidimensional gait analysis with pressure and motion sensors, as well as patient-reported outcomes (PROs), according to a standardized protocol. A feature selection ensemble (FS-Ensemble) was developed to improve the classification performance. The FS-Ensemble consisted of four filtering methods: Chi-square test, information gain, minimum redundancy maximum relevance and ReliefF. Gaussian Naive Bayes, Decision Tree, k-Nearest Neighbor, and Support Vector Machines (SVM) were used to identify FOF. RESULTS The descriptive analysis showed that 37 % of the 1240 pwMS had a FOF (n = 458; age: 51 ± 16 years, 76 % women, median EDSS: 4.0). The FS-Ensemble improved classification performance in most cases. The SVM showed the best performance of the four classification models in detecting FOF. The PROs showed the best F1 scores (Early Mobility Impairment Questionnaire F1 = 0.81 ± 0.00 and 12-item Multiple Sclerosis Scale F1 = 0.80 ± 0.00). CONCLUSION FOF is an important psychological risk factor associated with an increased risk of falls. To integrate a functional early warning system for fall detection into MS management and progression monitoring, it is necessary to detect the relevant gait parameters as well as assessment methods. In this context, ML strategies allow the integration of gait parameters from clinical routine to support the initiation of early rehabilitation measures and adaptation of course-modifying therapeutics. The results of this study confirm that patients' self-assessments play an important role in disease management.
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Affiliation(s)
- Paula Schumann
- Institute of Biomedical Engineering, TU Dresden, Fetscherstr. 29, Dresden 01307, Germany
| | - Katrin Trentzsch
- Center of Clinical Neuroscience, Neurological Clinic, University Hospital Carl Gustav Carus, TU Dresden, Fetscherstr. 74, Dresden 01307, Germany
| | - Heidi Stölzer-Hutsch
- Center of Clinical Neuroscience, Neurological Clinic, University Hospital Carl Gustav Carus, TU Dresden, Fetscherstr. 74, Dresden 01307, Germany
| | - Thurid Jochim
- Institute of Biomedical Engineering, TU Dresden, Fetscherstr. 29, Dresden 01307, Germany
| | - Maria Scholz
- Center of Clinical Neuroscience, Neurological Clinic, University Hospital Carl Gustav Carus, TU Dresden, Fetscherstr. 74, Dresden 01307, Germany
| | - Hagen Malberg
- Institute of Biomedical Engineering, TU Dresden, Fetscherstr. 29, Dresden 01307, Germany
| | - Tjalf Ziemssen
- Center of Clinical Neuroscience, Neurological Clinic, University Hospital Carl Gustav Carus, TU Dresden, Fetscherstr. 74, Dresden 01307, Germany.
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Przewocki J, Kossiński D, Łukaszuk A, Jakiel G, Wocławek-Potocka I, Ołdziej S, Łukaszuk K. Follicular Fluid Proteomic Analysis to Identify Predictive Markers of Normal Embryonic Development. Int J Mol Sci 2024; 25:8431. [PMID: 39126000 PMCID: PMC11313438 DOI: 10.3390/ijms25158431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2024] [Revised: 07/22/2024] [Accepted: 07/26/2024] [Indexed: 08/12/2024] Open
Abstract
Ageing populations, mass "baby-free" policies and children born to mothers at the age at which they are biologically expected to become grandmothers are growing problems in most developed societies. Therefore, any opportunity to improve the quality of infertility treatments seems important for the survival of societies. The possibility of indirectly studying the quality of developing oocytes by examining their follicular fluids (hFFs) offers new opportunities for progress in our understanding the processes of final oocyte maturation and, consequently, for predicting the quality of the resulting embryos and personalising their culture. Using mass spectrometry, we studied follicular fluids collected individually during in vitro fertilisation and compared their composition with the quality of the resulting embryos. We analysed 110 follicular fluids from 50 oocyte donors, from which we obtained 44 high-quality, 39 medium-quality, and 27 low-quality embryos. We identified 2182 proteins by Sequential Window Acquisition of all Theoretical Mass Spectra (SWATH-MS) using a TripleTOF 5600+ hybrid mass spectrometer, of which 484 were suitable for quantification. We were able to identify several proteins whose concentrations varied between the follicular fluids of different oocytes from the same patient and between patients. Among them, the most important appear to be immunoglobulin heavy constant alpha 1 (IgA1hc) and dickkopf-related protein 3. The first one is found at higher concentrations in hFFs from which oocytes develop into poor-quality embryos, the other one exhibits the opposite pattern. None of these have, so far, had any specific links to fertility disorders. In light of these findings, these proteins should be considered a primary target for research aimed at developing a diagnostic tool for oocyte quality control and pre-fertilisation screening. This is particularly important in cases where the fertilisation of each egg is not an option for ethical or other reasons, or in countries where it is prohibited by law.
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Affiliation(s)
- Janusz Przewocki
- Institute of Mathematics, University of Gdansk, 80-308 Gdańsk, Poland
- iYoni App—For Fertility Treatment, LifeBite, 10-763 Olsztyn, Poland; (D.K.); (K.Ł.)
| | - Dominik Kossiński
- iYoni App—For Fertility Treatment, LifeBite, 10-763 Olsztyn, Poland; (D.K.); (K.Ł.)
| | - Adam Łukaszuk
- Edinburgh Medical School, College of Medicine and Veterinary Medicine, The University of Edinburgh, 47 Little France Crescent, Edinburgh EH25 9RG, UK
| | - Grzegorz Jakiel
- Invicta Research and Development Center, 81-740 Sopot, Poland
- First Department of Obstetrics and Gynaecology, Centre of Postgraduate Medical Education, 01-004 Warsaw, Poland
| | - Izabela Wocławek-Potocka
- Department of Gamete and Embryo Biology, Institute of Animal Reproduction and Food Research, Polish Academy of Sciences, 10-748 Olsztyn, Poland;
| | - Stanisław Ołdziej
- Intercollegiate Faculty of Biotechnology UG & MUG, University of Gdańsk, Abrahama 58, 80-307 Gdańsk, Poland;
| | - Krzysztof Łukaszuk
- iYoni App—For Fertility Treatment, LifeBite, 10-763 Olsztyn, Poland; (D.K.); (K.Ł.)
- Department of Obstetrics and Gynecology Nursing, Medical University of Gdańsk, 80-210 Gdańsk, Poland
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Turfan D, Altunkaynak B, Yeniay Ö. A New Filter Approach Based on Effective Ranges for Classification of Gene Expression Data. BIG DATA 2024; 12:312-330. [PMID: 37668992 DOI: 10.1089/big.2022.0086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/06/2023]
Abstract
Over the years, many studies have been carried out to reduce and eliminate the effects of diseases on human health. Gene expression data sets play a critical role in diagnosing and treating diseases. These data sets consist of thousands of genes and a small number of sample sizes. This situation creates the curse of dimensionality and it becomes problematic to analyze such data sets. One of the most effective strategies to solve this problem is feature selection methods. Feature selection is a preprocessing step to improve classification performance by selecting the most relevant and informative features while increasing the accuracy of classification. In this article, we propose a new statistically based filter method for the feature selection approach named Effective Range-based Feature Selection Algorithm (FSAER). As an extension of the previous Effective Range based Gene Selection (ERGS) and Improved Feature Selection based on Effective Range (IFSER) algorithms, our novel method includes the advantages of both methods while taking into account the disjoint area. To illustrate the efficacy of the proposed algorithm, the experiments have been conducted on six benchmark gene expression data sets. The results of the FSAER and the other filter methods have been compared in terms of classification accuracies to demonstrate the effectiveness of the proposed method. For classification methods, support vector machines, naive Bayes classifier, and k-nearest neighbor algorithms have been used.
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Affiliation(s)
- Derya Turfan
- Department of Statistics, Hacettepe University, Ankara, Turkey
| | | | - Özgür Yeniay
- Department of Statistics, Hacettepe University, Ankara, Turkey
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10
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Zhang Q, Lin Y, Lin D, Lin X, Liu M, Tao H, Wu J, Wang T, Wang C, Feng S. Non-invasive screening and subtyping for breast cancer by serum SERS combined with LGB-DNN algorithms. Talanta 2024; 275:126136. [PMID: 38692045 DOI: 10.1016/j.talanta.2024.126136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 04/06/2024] [Accepted: 04/19/2024] [Indexed: 05/03/2024]
Abstract
Early detection of breast cancer and its molecular subtyping is crucial for guiding clinical treatment and improving survival rate. Current diagnostic methods for breast cancer are invasive, time consuming and complicated. In this work, an optical detection method integrating surface-enhanced Raman spectroscopy (SERS) technology with feature selection and deep learning algorithm was developed for identifying serum components and building diagnostic model, with the aim of efficient and accurate noninvasive screening of breast cancer. First, the high quality of serum SERS spectra from breast cancer (BC), breast benign disease (BBD) patients and healthy controls (HC) were obtained. Chi-square tests were conducted to exclude confounding factors, enhancing the reliability of the study. Then, LightGBM (LGB) algorithm was used as the base model to retain useful features to significantly improve classification performance. The DNN algorithm was trained through backpropagation, adjusting the weights and biases between neurons to improve the network's predictive ability. In comparison to traditional machine learning algorithms, this method provided more accurate information for breast cancer classification, with classification accuracies of 91.38 % for BC and BBD, and 96.40 % for BC, BBD, and HC. Furthermore, the accuracies of 90.11 % for HR+/HR- and 88.89 % for HER2+/HER2- can be reached when evaluating BC patients' molecular subtypes. These results demonstrate that serum SERS combined with powerful LGB-DNN algorithm would provide a supplementary method for clinical breast cancer screening.
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Affiliation(s)
- Qiyi Zhang
- Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, Fujian, 350117, China
| | - Yuxiang Lin
- Department of Breast Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian, 350001, China; Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian, 350001, China; Breast Cancer Institute, Fujian Medical University, Fuzhou, Fujian, 350001, China
| | - Duo Lin
- Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, Fujian, 350117, China
| | - Xueliang Lin
- Fujian Provincial Key Laboratory for Advanced Micro-nano Photonics Technology and Devices, Quanzhou Normal University, Quanzhou, 362000, China
| | - Miaomiao Liu
- Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, Fujian, 350117, China
| | - Hong Tao
- Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, Fujian, 350117, China
| | - Jinxun Wu
- Department of Pathology, Fuzhou Lianjiang Country Hospital, Fuzhou, Fujian, 350500, China
| | - Tingyin Wang
- Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, Fujian, 350117, China.
| | - Chuan Wang
- Department of Breast Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian, 350001, China; Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian, 350001, China; Breast Cancer Institute, Fujian Medical University, Fuzhou, Fujian, 350001, China.
| | - Shangyuan Feng
- Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, Fujian, 350117, China.
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11
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Xu T, Xu Y, Yang S, Li B, Zhang W. Learning Accurate Label-Specific Features From Partially Multilabeled Data. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:10436-10450. [PMID: 37022887 DOI: 10.1109/tnnls.2023.3241921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Feature selection is an effective dimensionality reduction technique, which can speed up an algorithm and improve model performance such as predictive accuracy and result comprehensibility. The study of selecting label-specific features for each class label has attracted considerable attention since each class label might be determined by some inherent characteristics, where precise label information is required to guide label-specific feature selection. However, obtaining noise-free labels is quite difficult and impractical. In reality, each instance is often annotated by a candidate label set that comprises multiple ground-truth labels and other false-positive labels, termed partial multilabel (PML) learning scenario. Here, false-positive labels concealed in a candidate label set might induce the selection of false label-specific features while masking the intrinsic label correlations, which misleads the selection of relevant features and compromises the selection performance. To address this issue, a novel two-stage partial multilabel feature selection (PMLFS) approach is proposed, which elicits credible labels to guide accurate label-specific feature selection. First, the label confidence matrix is learned to help elicit ground-truth labels from the candidate label set via the label structure reconstruction strategy, each element of which indicates how likely a class label is ground truth. After that, based on distilled credible labels, a joint selection model, including label-specific feature learner and common feature learner, is designed to learn accurate label-specific features to each class label and common features for all class labels. Besides, label correlations are fused into the features selection process to facilitate the generation of an optimal feature subset. Extensive experimental results clearly validate the superiority of the proposed approach.
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12
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Du Y, Niu J, Xing Y, Li B, Calhoun VD. Neuroimage Analysis Methods and Artificial Intelligence Techniques for Reliable Biomarkers and Accurate Diagnosis of Schizophrenia: Achievements Made by Chinese Scholars Around the Past Decade. Schizophr Bull 2024:sbae110. [PMID: 38982882 DOI: 10.1093/schbul/sbae110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/11/2024]
Abstract
BACKGROUND AND HYPOTHESIS Schizophrenia (SZ) is characterized by significant cognitive and behavioral disruptions. Neuroimaging techniques, particularly magnetic resonance imaging (MRI), have been widely utilized to investigate biomarkers of SZ, distinguish SZ from healthy conditions or other mental disorders, and explore biotypes within SZ or across SZ and other mental disorders, which aim to promote the accurate diagnosis of SZ. In China, research on SZ using MRI has grown considerably in recent years. STUDY DESIGN The article reviews advanced neuroimaging and artificial intelligence (AI) methods using single-modal or multimodal MRI to reveal the mechanism of SZ and promote accurate diagnosis of SZ, with a particular emphasis on the achievements made by Chinese scholars around the past decade. STUDY RESULTS Our article focuses on the methods for capturing subtle brain functional and structural properties from the high-dimensional MRI data, the multimodal fusion and feature selection methods for obtaining important and sparse neuroimaging features, the supervised statistical analysis and classification for distinguishing disorders, and the unsupervised clustering and semi-supervised learning methods for identifying neuroimage-based biotypes. Crucially, our article highlights the characteristics of each method and underscores the interconnections among various approaches regarding biomarker extraction and neuroimage-based diagnosis, which is beneficial not only for comprehending SZ but also for exploring other mental disorders. CONCLUSIONS We offer a valuable review of advanced neuroimage analysis and AI methods primarily focused on SZ research by Chinese scholars, aiming to promote the diagnosis, treatment, and prevention of SZ, as well as other mental disorders, both within China and internationally.
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Affiliation(s)
- Yuhui Du
- School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, China
| | - Ju Niu
- School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, China
| | - Ying Xing
- School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, China
| | - Bang Li
- School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, China
| | - Vince D Calhoun
- The Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, 30303, GA, USA
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13
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Kırboğa KK, Işık M. Explainable artificial intelligence in the design of selective carbonic anhydrase I-II inhibitors via molecular fingerprinting. J Comput Chem 2024; 45:1530-1539. [PMID: 38491535 DOI: 10.1002/jcc.27335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 02/04/2024] [Accepted: 02/10/2024] [Indexed: 03/18/2024]
Abstract
Inhibiting the enzymes carbonic anhydrase I (CA I) and carbonic anhydrase II (CA II) presents a potential avenue for addressing nervous system ailments such as glaucoma and Alzheimer's disease. Our study explored harnessing explainable artificial intelligence (XAI) to unveil the molecular traits inherent in CA I and CA II inhibitors. The PubChem molecular fingerprints of these inhibitors, sourced from the ChEMBL database, were subjected to detailed XAI analysis. The study encompassed training 10 regression models using IC50 values, and their efficacy was gauged using metrics including R2, RMSE, and time taken. The Decision Tree Regressor algorithm emerged as the optimal performer (R2: 0.93, RMSE: 0.43, time-taken: 0.07). Furthermore, the PFI method unveiled key molecular features for CA I inhibitors, notably PubChemFP432 (C(O)N) and PubChemFP6978 (C(O)O). The SHAP analysis highlighted the significance of attributes like PubChemFP539 (C(O)NCC), PubChemFP601 (C(O)OCC), and PubChemFP432 (C(O)N) in CA I inhibitiotable n. Likewise, features for CA II inhibitors encompassed PubChemFP528(C(O)OCCN), PubChemFP791 (C(O)OCCC), PubChemFP696 (C(O)OCCCC), PubChemFP335 (C(O)NCCN), PubChemFP580 (C(O)NCCCN), and PubChemFP180 (C(O)NCCC), identified through SHAP analysis. The sulfonamide group (S), aromatic ring (A), and hydrogen bonding group (H) exert a substantial impact on CA I and CA II enzyme activities and IC50 values through the XAI approach. These insights into the CA I and CA II inhibitors are poised to guide future drug discovery efforts, serving as a beacon for innovative therapeutic interventions.
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Affiliation(s)
- Kevser Kübra Kırboğa
- Faculty of Engineering, Department of Bioengineering, Bilecik Seyh Edebali University, Bilecik, Turkey
- Bioengineering Department, Süleyman Demirel University, Isparta, Turkey
| | - Mesut Işık
- Faculty of Engineering, Department of Bioengineering, Bilecik Seyh Edebali University, Bilecik, Turkey
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14
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Zhou K, Xiao Z, Liu Q, Wang X, Huo J, Wu X, Zhao X, Feng X, Fu B, Xu P, Deng Y, Xiao W, Sun T, Da L. Comprehensive application of AI algorithms with TCR NGS data for glioma diagnosis. Sci Rep 2024; 14:15361. [PMID: 38965388 PMCID: PMC11224284 DOI: 10.1038/s41598-024-65305-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Accepted: 06/19/2024] [Indexed: 07/06/2024] Open
Abstract
T-cell receptor (TCR) detection can examine the extent of T-cell immune responses. Therefore, the article analyzed characteristic data of glioma obtained by DNA-based TCR high-throughput sequencing, to predict the disease with fewer biomarkers and higher accuracy. We downloaded data online and obtained six TCR-related diversity indices to establish a multidimensional classification system. By comparing actual presence of the 602 correlated sequences, we obtained two-dimensional and multidimensional datasets. Multiple classification methods were utilized for both datasets with the classification accuracy of multidimensional data slightly less to two-dimensional datasets. This study reduced the TCR β sequences through feature selection methods like RFECV (Recursive Feature Elimination with Cross-Validation). Consequently, using only the presence of these three sequences, the classification AUC value of 96.67% can be achieved. The combination of the three correlated TCR clones obtained at a source data threshold of 0.1 is: CASSLGGNTEAFF_TRBV12_TRBJ1-1, CASSYSDTGELFF_TRBV6_TRBJ2-2, and CASSLTGNTEAFF_TRBV12_TRBJ1-1. At 0.001, the combination is: CASSLGETQYF_TRBV12_TRBJ2-5, CASSLGGNQPQHF_TRBV12_TRBJ1-5, and CASSLSGNTIYF_TRBV12_TRBJ1-3. This method can serve as a potential diagnostic and therapeutic tool, facilitating diagnosis and treatment of glioma and other cancers.
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Affiliation(s)
- Kaiyue Zhou
- Department of Mathematics, School of Mathematical Sciences, Inner Mongolia University, Hohhot, China
| | - Zhengliang Xiao
- Department of Mathematics, School of Mathematical Sciences, Inner Mongolia University, Hohhot, China
| | - Qi Liu
- Department of Mathematics, School of Mathematical Sciences, Inner Mongolia University, Hohhot, China
| | - Xu Wang
- Department of Mathematics, School of Mathematical Sciences, Inner Mongolia University, Hohhot, China
| | - Jiaxin Huo
- Department of Mathematics, School of Mathematical Sciences, Inner Mongolia University, Hohhot, China
| | - Xiaoqi Wu
- Department of Mathematics, School of Mathematical Sciences, Inner Mongolia University, Hohhot, China
| | - Xiaoxiao Zhao
- Department of Mathematics, School of Mathematical Sciences, Inner Mongolia University, Hohhot, China
| | - Xiaohan Feng
- Department of Mathematics, School of Mathematical Sciences, Inner Mongolia University, Hohhot, China
| | - Baoyi Fu
- Department of Mathematics, School of Mathematical Sciences, Inner Mongolia University, Hohhot, China
| | - Pengfei Xu
- Hangzhou ImmuQuad Biotechnologies, LLC, Hangzhou, China
| | - Yunyun Deng
- Hangzhou ImmuQuad Biotechnologies, LLC, Hangzhou, China
| | - Wenwen Xiao
- Hangzhou ImmuQuad Biotechnologies, LLC, Hangzhou, China
| | - Tao Sun
- Hangzhou ImmuQuad Biotechnologies, LLC, Hangzhou, China.
- Institute of Wenzhou, Zhejiang University, Wenzhou, China.
| | - Lin Da
- Department of Mathematics, School of Mathematical Sciences, Inner Mongolia University, Hohhot, China.
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15
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Abbasi Holasou H, Panahi B, Shahi A, Nami Y. Integration of machine learning models with microsatellite markers: New avenue in world grapevine germplasm characterization. Biochem Biophys Rep 2024; 38:101678. [PMID: 38495412 PMCID: PMC10940787 DOI: 10.1016/j.bbrep.2024.101678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Revised: 02/09/2024] [Accepted: 02/27/2024] [Indexed: 03/19/2024] Open
Abstract
Development of efficient analytical techniques is required for effective interpretation of biological data to take novel hypotheses and finding the critical predictive patterns. Machine Learning algorithms provide a novel opportunity for development of low-cost and practical solutions in biology. In this study, we proposed a new integrated analytical approach using supervised machine learning algorithms and microsatellites data of worldwide vitis populations. A total of 1378 wild (V. vinifera spp. sylvestris) and cultivated (V. vinifera spp. sativa) accessions of grapevine were investigated using 20 microsatellite markers. Data cleaning, feature selection, and supervised machine learning classification models vis, Naive Bayes, Support Vector Machine (SVM) and Tree Induction methods were implied to find most indicative and diagnostic alleles to represent wild/cultivated and originated geography of each population. Our combined approaches showed microsatellite markers with the highest differentiating capacity and proved efficiency for our pipeline of classification and prediction of vitis accessions. Moreover, our study proposed the best combination of markers for better distinguishing of populations, which can be exploited in future germplasm conservation and breeding programs.
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Affiliation(s)
- Hossein Abbasi Holasou
- Department of Plant Breeding and Biotechnology, Faculty of Agriculture, University of Tabriz, Tabriz, Iran
| | - Bahman Panahi
- Department of Genomics, Branch for Northwest and West Region, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research, Education and Extension Organization (AREEO), Tabriz, Iran
| | - Ali Shahi
- Faculty of Agriculture (Meshgin Shahr Campus), Mohaghegh Ardabili University, Ardabil, Iran
| | - Yousef Nami
- Department of Food Biotechnology, Branch for Northwest and West Region, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research, Education and Extension Organization (AREEO), Tabriz, Iran
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16
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Culley S, Caballero AC, Burden JJ, Uhlmann V. Made to measure: An introduction to quantifying microscopy data in the life sciences. J Microsc 2024; 295:61-82. [PMID: 37269048 DOI: 10.1111/jmi.13208] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 05/25/2023] [Accepted: 05/26/2023] [Indexed: 06/04/2023]
Abstract
Images are at the core of most modern biological experiments and are used as a major source of quantitative information. Numerous algorithms are available to process images and make them more amenable to be measured. Yet the nature of the quantitative output that is useful for a given biological experiment is uniquely dependent upon the question being investigated. Here, we discuss the 3 main types of information that can be extracted from microscopy data: intensity, morphology, and object counts or categorical labels. For each, we describe where they come from, how they can be measured, and what may affect the relevance of these measurements in downstream data analysis. Acknowledging that what makes a measurement 'good' is ultimately down to the biological question being investigated, this review aims at providing readers with a toolkit to challenge how they quantify their own data and be critical of conclusions drawn from quantitative bioimage analysis experiments.
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Affiliation(s)
- Siân Culley
- Randall Centre for Cell and Molecular Biophysics, King's College London, London, UK
| | | | | | - Virginie Uhlmann
- European Bioinformatics Institute (EMBL-EBI), EMBL, Cambridge, UK
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17
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Mirjalili SR, Soltani S, Meybodi ZH, Marques-Vidal P, Firouzabadi DD, Eshraghi R, Restrepo D, Ghoshouni H, Sarebanhassanabadi M. Which surrogate insulin resistance indices best predict coronary artery disease? A machine learning approach. Cardiovasc Diabetol 2024; 23:214. [PMID: 38907271 PMCID: PMC11193173 DOI: 10.1186/s12933-024-02306-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 06/16/2024] [Indexed: 06/23/2024] Open
Abstract
BACKGROUND Various surrogate markers of insulin resistance have been developed, capable of predicting coronary artery disease (CAD) without the need to detect serum insulin. For accurate prediction, they depend only on glucose and lipid profiles, as well as anthropometric features. However, there is still no agreement on the most suitable one for predicting CAD. METHODS We followed a cohort of 2,000 individuals, ranging in age from 20 to 74, for a duration of 9.9 years. We utilized multivariate Cox proportional hazard models to investigate the association between TyG-index, TyG-BMI, TyG-WC, TG/HDL, plus METS-IR and the occurrence of CAD. The receiver operating curve (ROC) was employed to compare the predictive efficacy of these indices and their corresponding cutoff values for predicting CAD. We also used three distinct embedded feature selection methods: LASSO, Random Forest feature selection, and the Boruta algorithm, to evaluate and compare surrogate markers of insulin resistance in predicting CAD. In addition, we utilized the ceteris paribus profile on the Random Forest model to illustrate how the model's predictive performance is affected by variations in individual surrogate markers, while keeping all other factors consistent in a diagram. RESULTS The TyG-index was the only surrogate marker of insulin resistance that demonstrated an association with CAD in fully adjusted model (HR: 2.54, CI: 1.34-4.81). The association was more prominent in females. Moreover, it demonstrated the highest area under the ROC curve (0.67 [0.63-0.7]) in comparison to other surrogate indices for insulin resistance. All feature selection approaches concur that the TyG-index is the most reliable surrogate insulin resistance marker for predicting CAD. Based on the Ceteris paribus profile of Random Forest the predictive ability of the TyG-index increased steadily after 9 with a positive slope, without any decline or leveling off. CONCLUSION Due to the simplicity of assessing the TyG-index with routine biochemical assays and given that the TyG-index was the most effective surrogate insulin resistance index for predicting CAD based on our results, it seems suitable for inclusion in future CAD prevention strategies.
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Affiliation(s)
- Seyed Reza Mirjalili
- Yazd Cardiovascular Research Center, Non-Communicable Diseases Research Institute, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
| | - Sepideh Soltani
- Yazd Cardiovascular Research Center, Non-Communicable Diseases Research Institute, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
| | | | - Pedro Marques-Vidal
- Department of Internal Medicine, BH10-642, Rue du Bugnon 46, Rue du Bugnon 46, Lausanne, CH-1011, Switzerland
| | | | - Reza Eshraghi
- Student Research Committee, Kashan University of Medical Sciences, Kashan, Iran
| | - David Restrepo
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA
- Telematics Department, University of Cauca, Popayán, Cauca, Colombia
| | - Hamed Ghoshouni
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Mohammadtaghi Sarebanhassanabadi
- Yazd Cardiovascular Research Center, Non-Communicable Diseases Research Institute, Shahid Sadoughi University of Medical Sciences, Yazd, Iran.
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18
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Nazari MJ, Shalbafan M, Eissazade N, Khalilian E, Vahabi Z, Masjedi N, Ghidary SS, Saadat M, Sadegh-Zadeh SA. A machine learning approach for differentiating bipolar disorder type II and borderline personality disorder using electroencephalography and cognitive abnormalities. PLoS One 2024; 19:e0303699. [PMID: 38905185 PMCID: PMC11192371 DOI: 10.1371/journal.pone.0303699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 04/29/2024] [Indexed: 06/23/2024] Open
Abstract
This study addresses the challenge of differentiating between bipolar disorder II (BD II) and borderline personality disorder (BPD), which is complicated by overlapping symptoms. To overcome this, a multimodal machine learning approach was employed, incorporating both electroencephalography (EEG) patterns and cognitive abnormalities for enhanced classification. Data were collected from 45 participants, including 20 with BD II and 25 with BPD. Analysis involved utilizing EEG signals and cognitive tests, specifically the Wisconsin Card Sorting Test and Integrated Cognitive Assessment. The k-nearest neighbors (KNN) algorithm achieved a balanced accuracy of 93%, with EEG features proving to be crucial, while cognitive features had a lesser impact. Despite the strengths, such as diverse model usage, it's important to note limitations, including a small sample size and reliance on DSM diagnoses. The study suggests that future research should explore multimodal data integration and employ advanced techniques to improve classification accuracy and gain a better understanding of the neurobiological distinctions between BD II and BPD.
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Affiliation(s)
- Mohammad-Javad Nazari
- Computer Science and Mathematics Department, Amirkabir University of Technology, Tehran, Iran
| | - Mohammadreza Shalbafan
- Department of Psychiatry, Psychosocial Health Research Institute (PHRI), Mental Health Research Center, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
- Institute for Cognitive Sciences Studies, Brain and Cognition Clinic, Tehran, Iran
| | - Negin Eissazade
- Student Research Committee, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Elham Khalilian
- Department of Psychiatry, Tehran University of Medical Sciences, Tehran, Iran
| | - Zahra Vahabi
- Neuropsychiatry Department, Tehran University of Medical Sciences, Tehran, Iran
| | - Neda Masjedi
- Department of Psychiatry, Tehran University of Medical Sciences, Tehran, Iran
| | - Saeed Shiry Ghidary
- Computer Science and Mathematics Department, Amirkabir University of Technology, Tehran, Iran
| | - Mozafar Saadat
- Department of Mechanical Engineering, School of Engineering, University of Birmingham, Birmingham, United Kingdom
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19
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Bang JH, Kim EH, Kim HJ, Chung JW, Seo WK, Kim GM, Lee DH, Kim H, Bang OY. Machine Learning-Based Etiologic Subtyping of Ischemic Stroke Using Circulating Exosomal microRNAs. Int J Mol Sci 2024; 25:6761. [PMID: 38928481 PMCID: PMC11203849 DOI: 10.3390/ijms25126761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2024] [Revised: 06/07/2024] [Accepted: 06/14/2024] [Indexed: 06/28/2024] Open
Abstract
Ischemic stroke is a major cause of mortality worldwide. Proper etiological subtyping of ischemic stroke is crucial for tailoring treatment strategies. This study explored the utility of circulating microRNAs encapsulated in extracellular vesicles (EV-miRNAs) to distinguish the following ischemic stroke subtypes: large artery atherosclerosis (LAA), cardioembolic stroke (CES), and small artery occlusion (SAO). Using next-generation sequencing (NGS) and machine-learning techniques, we identified differentially expressed miRNAs (DEMs) associated with each subtype. Through patient selection and diagnostic evaluation, a cohort of 70 patients with acute ischemic stroke was classified: 24 in the LAA group, 24 in the SAO group, and 22 in the CES group. Our findings revealed distinct EV-miRNA profiles among the groups, suggesting their potential as diagnostic markers. Machine-learning models, particularly logistic regression models, exhibited a high diagnostic accuracy of 92% for subtype discrimination. The collective influence of multiple miRNAs was more crucial than that of individual miRNAs. Additionally, bioinformatics analyses have elucidated the functional implications of DEMs in stroke pathophysiology, offering insights into the underlying mechanisms. Despite limitations like sample size constraints and retrospective design, our study underscores the promise of EV-miRNAs coupled with machine learning for ischemic stroke subtype classification. Further investigations are warranted to validate the clinical utility of the identified EV-miRNA biomarkers in stroke patients.
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Affiliation(s)
- Ji Hoon Bang
- Global School of Media, College of IT, Soongsil University, Seoul 06978, Republic of Korea;
| | - Eun Hee Kim
- S&E Bio, Inc., Seoul 05855, Republic of Korea
| | - Hyung Jun Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea
| | - Jong-Won Chung
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea
| | - Woo-Keun Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea
| | - Gyeong-Moon Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea
| | - Dong-Ho Lee
- Calth, Inc., Seongnam-si 13449, Republic of Korea
| | - Heewon Kim
- Global School of Media, College of IT, Soongsil University, Seoul 06978, Republic of Korea;
| | - Oh Young Bang
- S&E Bio, Inc., Seoul 05855, Republic of Korea
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea
- Department of Health Sciences and Technology, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Seoul 06351, Republic of Korea
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20
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Lee SH, Cha JM, Shin SJ. Personalized prediction of survival rate with combination of penalized Cox models in patients with colorectal cancer. Medicine (Baltimore) 2024; 103:e38584. [PMID: 38875378 PMCID: PMC11175897 DOI: 10.1097/md.0000000000038584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/16/2024] Open
Abstract
The investigation into individual survival rates within the patient population was typically conducted using the Cox proportional hazards model. This study was aimed to evaluate the performance of machine learning algorithm in predicting survival rates more than 5 years for individual patients with colorectal cancer. A total of 475 patients with colorectal cancer (CRC) and complete data who had underwent surgery for CRC were analyze to measure individual's survival rate more than 5 years using a machine learning based on penalized Cox regression. We conducted thorough calculations to measure the individual's survival rate more than 5 years for performance evaluation. The receiver operating characteristic curves for the LASSO penalized model, the SCAD penalized model, the unpenalized model, and the RSF model were analyzed. The least absolute shrinkage and selection operator penalized model displayed a mean AUC of 0.67 ± 0.06, the smoothly clipped absolute deviation penalized model exhibited a mean AUC of 0.65 ± 0.07, the unpenalized model showed a mean AUC of 0.64 ± 0.09. Notably, the random survival forests model outperformed the others, demonstrating the most favorable performance evaluation with a mean AUC of 0.71 ± 0.05. Compared to the conventional unpenalized Cox model, recent machine learning techniques (LASSO, SCAD, RSF) showed advantages for data interpretation.
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Affiliation(s)
- Seon Hwa Lee
- Department of Data Statistics, Graduate School, Korea University, Seoul, Republic of Korea
- Medical Big Data Research Center, Research Institute of Clinical Medicine, Kyung Hee University Hospital at Gangdong, Seoul, Republic of Korea
| | - Jae Myung Cha
- Department of Internal Medicine, Kyung Hee University Hospital Gang Dong, College of Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - Seung Jun Shin
- Department of Statistics, Korea University, Seoul, Republic of Korea
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21
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Qiu C, Su K, Luo Z, Tian Q, Zhao L, Wu L, Deng H, Shen H. Developing and comparing deep learning and machine learning algorithms for osteoporosis risk prediction. Front Artif Intell 2024; 7:1355287. [PMID: 38919268 PMCID: PMC11196804 DOI: 10.3389/frai.2024.1355287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 05/31/2024] [Indexed: 06/27/2024] Open
Abstract
Introduction Osteoporosis, characterized by low bone mineral density (BMD), is an increasingly serious public health issue. So far, several traditional regression models and machine learning (ML) algorithms have been proposed for predicting osteoporosis risk. However, these models have shown relatively low accuracy in clinical implementation. Recently proposed deep learning (DL) approaches, such as deep neural network (DNN), which can discover knowledge from complex hidden interactions, offer a new opportunity to improve predictive performance. In this study, we aimed to assess whether DNN can achieve a better performance in osteoporosis risk prediction. Methods By utilizing hip BMD and extensive demographic and routine clinical data of 8,134 subjects with age more than 40 from the Louisiana Osteoporosis Study (LOS), we developed and constructed a novel DNN framework for predicting osteoporosis risk and compared its performance in osteoporosis risk prediction with four conventional ML models, namely random forest (RF), artificial neural network (ANN), k-nearest neighbor (KNN), and support vector machine (SVM), as well as a traditional regression model termed osteoporosis self-assessment tool (OST). Model performance was assessed by area under 'receiver operating curve' (AUC) and accuracy. Results By using 16 discriminative variables, we observed that the DNN approach achieved the best predictive performance (AUC = 0.848) in classifying osteoporosis (hip BMD T-score ≤ -1.0) and non-osteoporosis risk (hip BMD T-score > -1.0) subjects, compared to the other approaches. Feature importance analysis showed that the top 10 most important variables identified by the DNN model were weight, age, gender, grip strength, height, beer drinking, diastolic pressure, alcohol drinking, smoke years, and economic level. Furthermore, we performed subsampling analysis to assess the effects of varying number of sample size and variables on the predictive performance of these tested models. Notably, we observed that the DNN model performed equally well (AUC = 0.846) even by utilizing only the top 10 most important variables for osteoporosis risk prediction. Meanwhile, the DNN model can still achieve a high predictive performance (AUC = 0.826) when sample size was reduced to 50% of the original dataset. Conclusion In conclusion, we developed a novel DNN model which was considered to be an effective algorithm for early diagnosis and intervention of osteoporosis in the aging population.
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Affiliation(s)
| | | | | | | | | | | | - Hongwen Deng
- Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA, United States
| | - Hui Shen
- Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA, United States
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22
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Li M, Cai Y, Zhang Y, Carlson PE, Dong R, Gong Z, Zhang Y, Li K. Impacts of habitat alteration on macroinvertebrates in large shallow lakes: An application of a macroinvertebrate-based multimetric index. INTEGRATED ENVIRONMENTAL ASSESSMENT AND MANAGEMENT 2024. [PMID: 38837538 DOI: 10.1002/ieam.4966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 05/13/2024] [Accepted: 05/13/2024] [Indexed: 06/07/2024]
Abstract
Habitat plays a crucial role in shaping the macroinvertebrate community structure in large shallow lakes. In the pursuit of improving the health of freshwater ecosystems, it is imperative to consider their habitat characteristics. To evaluate the impact of habitat variations on lake ecological health, we developed a macroinvertebrate-based multimetric index (MMI) for both the pelagic and littoral zones of Lake Hongze. Additionally, we employed structural equation models to explore the influence of utilization or phytoplankton biomass on ecological health. Historical data served as reference conditions for the pelagic. Seven key attributes were selected for the pelagic MMI, that is, Biological Monitoring Working Party (BMWP), the percentage of Mollusca taxa, the percentage of filter-collector taxa, the percentage of predator taxa, the percentage of gather-collector taxa, and the percentage of sensitive taxa and functional dispersion. The least minimally disturbed conditions and the best attainable conditions were used to develop the littoral. Four key metrics, that is, the percentage of scraper abundance, Mollusca taxa, Biological Pollution Index, and BMWP, were integrated into the littoral MMI. The assessment based on MMI revealed a "poor" health status for the pelagic zone and a "fair" health status for the littoral zone. These findings underscore the high applicability and efficacy of MMIs in assessing and monitoring ecological health in Lake Hongze. Notably, functional feeding groups exhibited heightened sensitivity to disturbance in both zones. Moreover, sediment organic matter strongly influenced the pelagic ecological health, while chlorophyll a and transparency emerged as primary factors influencing the littoral zone, attributable to varying littoral zone utilization. Integr Environ Assess Manag 2024;00:1-11. © 2024 SETAC.
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Affiliation(s)
- Mingjie Li
- Key Laboratory of Lake and Watershed Science for Water Security, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, Jiangsu, China
| | - Yongjiu Cai
- Key Laboratory of Lake and Watershed Science for Water Security, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, Jiangsu, China
| | - Ying Zhang
- Water Resources Planning Bureau of Jiangsu Province, Nanjing, China
| | - Peter E Carlson
- Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Rui Dong
- Key Laboratory of Lake and Watershed Science for Water Security, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, Jiangsu, China
| | - Zhijun Gong
- Key Laboratory of Lake and Watershed Science for Water Security, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, Jiangsu, China
| | - You Zhang
- Key Laboratory of Lake and Watershed Science for Water Security, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, Jiangsu, China
- Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Kuanyi Li
- Key Laboratory of Lake and Watershed Science for Water Security, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, Jiangsu, China
- Sino-Danish College, University of Chinese Academy of Sciences, Beijing, China
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23
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Pradhan UK, Meher PK, Naha S, Das R, Gupta A, Parsad R. ProkDBP: Toward more precise identification of prokaryotic DNA binding proteins. Protein Sci 2024; 33:e5015. [PMID: 38747369 PMCID: PMC11094783 DOI: 10.1002/pro.5015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 04/18/2024] [Accepted: 04/21/2024] [Indexed: 05/19/2024]
Abstract
Prokaryotic DNA binding proteins (DBPs) play pivotal roles in governing gene regulation, DNA replication, and various cellular functions. Accurate computational models for predicting prokaryotic DBPs hold immense promise in accelerating the discovery of novel proteins, fostering a deeper understanding of prokaryotic biology, and facilitating the development of therapeutics targeting for potential disease interventions. However, existing generic prediction models often exhibit lower accuracy in predicting prokaryotic DBPs. To address this gap, we introduce ProkDBP, a novel machine learning-driven computational model for prediction of prokaryotic DBPs. For prediction, a total of nine shallow learning algorithms and five deep learning models were utilized, with the shallow learning models demonstrating higher performance metrics compared to their deep learning counterparts. The light gradient boosting machine (LGBM), coupled with evolutionarily significant features selected via random forest variable importance measure (RF-VIM) yielded the highest five-fold cross-validation accuracy. The model achieved the highest auROC (0.9534) and auPRC (0.9575) among the 14 machine learning models evaluated. Additionally, ProkDBP demonstrated substantial performance with an independent dataset, exhibiting higher values of auROC (0.9332) and auPRC (0.9371). Notably, when benchmarked against several cutting-edge existing models, ProkDBP showcased superior predictive accuracy. Furthermore, to promote accessibility and usability, ProkDBP (https://iasri-sg.icar.gov.in/prokdbp/) is available as an online prediction tool, enabling free access to interested users. This tool stands as a significant contribution, enhancing the repertoire of resources for accurate and efficient prediction of prokaryotic DBPs.
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Affiliation(s)
- Upendra Kumar Pradhan
- Division of Statistical GeneticsICAR‐Indian Agricultural Statistics Research Institute, PUSANew DelhiIndia
| | - Prabina Kumar Meher
- Division of Statistical GeneticsICAR‐Indian Agricultural Statistics Research Institute, PUSANew DelhiIndia
| | - Sanchita Naha
- Division of Computer ApplicationsICAR‐Indian Agricultural Statistics Research Institute, PUSANew DelhiIndia
| | - Ritwika Das
- Division of Agricultural BioinformaticsICAR‐Indian Agricultural Statistics Research Institute, PUSANew DelhiIndia
| | - Ajit Gupta
- Division of Statistical GeneticsICAR‐Indian Agricultural Statistics Research Institute, PUSANew DelhiIndia
| | - Rajender Parsad
- ICAR‐Indian Agricultural Statistics Research Institute, PUSANew DelhiIndia
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24
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Chowa SS, Azam S, Montaha S, Bhuiyan MRI, Jonkman M. Improving the Automated Diagnosis of Breast Cancer with Mesh Reconstruction of Ultrasound Images Incorporating 3D Mesh Features and a Graph Attention Network. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:1067-1085. [PMID: 38361007 DOI: 10.1007/s10278-024-00983-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 11/17/2023] [Accepted: 12/11/2023] [Indexed: 02/17/2024]
Abstract
This study proposes a novel approach for breast tumor classification from ultrasound images into benign and malignant by converting the region of interest (ROI) of a 2D ultrasound image into a 3D representation using the point-e system, allowing for in-depth analysis of underlying characteristics. Instead of relying solely on 2D imaging features, this method extracts 3D mesh features that describe tumor patterns more precisely. Ten informative and medically relevant mesh features are extracted and assessed with two feature selection techniques. Additionally, a feature pattern analysis has been conducted to determine the feature's significance. A feature table with dimensions of 445 × 12 is generated and a graph is constructed, considering the rows as nodes and the relationships among the nodes as edges. The Spearman correlation coefficient method is employed to identify edges between the strongly connected nodes (with a correlation score greater than or equal to 0.7), resulting in a graph containing 56,054 edges and 445 nodes. A graph attention network (GAT) is proposed for the classification task and the model is optimized with an ablation study, resulting in the highest accuracy of 99.34%. The performance of the proposed model is compared with ten machine learning (ML) models and one-dimensional convolutional neural network where the test accuracy of these models ranges from 73 to 91%. Our novel 3D mesh-based approach, coupled with the GAT, yields promising performance for breast tumor classification, outperforming traditional models, and has the potential to reduce time and effort of radiologists providing a reliable diagnostic system.
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Affiliation(s)
- Sadia Sultana Chowa
- Faculty of Science and Technology, Charles Darwin University, Casuarina, NT, 0909, Australia
| | - Sami Azam
- Faculty of Science and Technology, Charles Darwin University, Casuarina, NT, 0909, Australia.
| | - Sidratul Montaha
- Faculty of Science and Technology, Charles Darwin University, Casuarina, NT, 0909, Australia
| | - Md Rahad Islam Bhuiyan
- Faculty of Science and Technology, Charles Darwin University, Casuarina, NT, 0909, Australia
| | - Mirjam Jonkman
- Faculty of Science and Technology, Charles Darwin University, Casuarina, NT, 0909, Australia
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25
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Islam MA, Majumder MZH, Miah MS, Jannaty S. Precision healthcare: A deep dive into machine learning algorithms and feature selection strategies for accurate heart disease prediction. Comput Biol Med 2024; 176:108432. [PMID: 38744014 DOI: 10.1016/j.compbiomed.2024.108432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 04/06/2024] [Accepted: 04/07/2024] [Indexed: 05/16/2024]
Abstract
This paper presents a comprehensive exploration of machine learning algorithms (MLAs) and feature selection techniques for accurate heart disease prediction (HDP) in modern healthcare. By focusing on diverse datasets encompassing various challenges, the research sheds light on optimal strategies for early detection. MLAs such as Decision Trees (DT), Random Forests (RF), Support Vector Machines (SVM), Gaussian Naive Bayes (NB), and others were studied, with precision and recall metrics emphasized for robust predictions. Our study addresses challenges in real-world data through data cleaning and one-hot encoding, enhancing the integrity of our predictive models. Feature extraction techniques-Recursive Feature Extraction (RFE), Principal Component Analysis (PCA), and univariate feature selection-play a crucial role in identifying relevant features and reducing data dimensionality. Our findings showcase the impact of these techniques on improving prediction accuracy. Optimized models for each dataset have been achieved through grid search hyperparameter tuning, with configurations meticulously outlined. Notably, a remarkable 99.12 % accuracy was achieved on the first Kaggle dataset, showcasing the potential for accurate HDP. Model robustness across diverse datasets was highlighted, with caution against overfitting. The study emphasizes the need for validation of unseen data and encourages ongoing research for generalizability. Serving as a practical guide, this research aids researchers and practitioners in HDP model development, influencing clinical decisions and healthcare resource allocation. By providing insights into effective algorithms and techniques, the paper contributes to reducing heart disease-related morbidity and mortality, supporting the healthcare community's ongoing efforts.
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Affiliation(s)
- Md Ariful Islam
- Department of Robotics and Mechatronics Engineering, University of Dhaka, Dhaka, 1000, Bangladesh.
| | | | - Md Sohel Miah
- Department of Computer Science and Technology, Moulvibazar Polytechnic Institute, Bangladesh
| | - Sumaia Jannaty
- Gonoshasthaya Samaj Vittik Medical College, Savar, Dhaka, Bangladesh
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26
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Nie Z, Gao M, Jin X, Rao Y, Zhang X. MFPINC: prediction of plant ncRNAs based on multi-source feature fusion. BMC Genomics 2024; 25:531. [PMID: 38816689 PMCID: PMC11137975 DOI: 10.1186/s12864-024-10439-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 05/21/2024] [Indexed: 06/01/2024] Open
Abstract
Non-coding RNAs (ncRNAs) are recognized as pivotal players in the regulation of essential physiological processes such as nutrient homeostasis, development, and stress responses in plants. Common methods for predicting ncRNAs are susceptible to significant effects of experimental conditions and computational methods, resulting in the need for significant investment of time and resources. Therefore, we constructed an ncRNA predictor(MFPINC), to predict potential ncRNA in plants which is based on the PINC tool proposed by our previous studies. Specifically, sequence features were carefully refined using variance thresholding and F-test methods, while deep features were extracted and feature fusion were performed by applying the GRU model. The comprehensive evaluation of multiple standard datasets shows that MFPINC not only achieves more comprehensive and accurate identification of gene sequences, but also significantly improves the expressive and generalization performance of the model, and MFPINC significantly outperforms the existing competing methods in ncRNA identification. In addition, it is worth mentioning that our tool can also be found on Github ( https://github.com/Zhenj-Nie/MFPINC ) the data and source code can also be downloaded for free.
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Affiliation(s)
- Zhenjun Nie
- School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei, 230036, China
| | - Mengqing Gao
- School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei, 230036, China
| | - Xiu Jin
- School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei, 230036, China
- Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, Hefei, 230036, China
| | - Yuan Rao
- School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei, 230036, China
- Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, Hefei, 230036, China
| | - Xiaodan Zhang
- School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei, 230036, China.
- Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, Hefei, 230036, China.
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27
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Bitto V, Hönscheid P, Besso MJ, Sperling C, Kurth I, Baumann M, Brors B. Enhancing mass spectrometry imaging accessibility using convolutional autoencoders for deriving hypoxia-associated peptides from tumors. NPJ Syst Biol Appl 2024; 10:57. [PMID: 38802379 PMCID: PMC11130291 DOI: 10.1038/s41540-024-00385-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Accepted: 05/13/2024] [Indexed: 05/29/2024] Open
Abstract
Mass spectrometry imaging (MSI) allows to study cancer's intratumoral heterogeneity through spatially-resolved peptides, metabolites and lipids. Yet, in biomedical research MSI is rarely used for biomarker discovery. Besides its high dimensionality and multicollinearity, mass spectrometry (MS) technologies typically output mass-to-charge ratio values but not the biochemical compounds of interest. Our framework makes particularly low-abundant signals in MSI more accessible. We utilized convolutional autoencoders to aggregate features associated with tumor hypoxia, a parameter with significant spatial heterogeneity, in cancer xenograft models. We highlight that MSI captures these low-abundant signals and that autoencoders can preserve them in their latent space. The relevance of individual hyperparameters is demonstrated through ablation experiments, and the contribution from original features to latent features is unraveled. Complementing MSI with tandem MS from the same tumor model, multiple hypoxia-associated peptide candidates were derived. Compared to random forests alone, our autoencoder approach yielded more biologically relevant insights for biomarker discovery.
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Affiliation(s)
- Verena Bitto
- Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
- Division of Radiooncology/Radiobiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
- HIDSS4Health - Helmholtz Information and Data Science School for Health, Karlsruhe/Heidelberg, Heidelberg, Germany.
- Faculty for Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany.
| | - Pia Hönscheid
- National Center for Tumor Diseases (NCT), Partner Site Dresden, German Cancer Research Center (DKFZ), Heidelberg, Germany
- University Hospital Carl Gustav Carus (UKD), Technische Universität Dresden, Institute of Pathology, Dresden, Germany
- Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - María José Besso
- Division of Radiooncology/Radiobiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Christian Sperling
- National Center for Tumor Diseases (NCT), Partner Site Dresden, German Cancer Research Center (DKFZ), Heidelberg, Germany
- University Hospital Carl Gustav Carus (UKD), Technische Universität Dresden, Institute of Pathology, Dresden, Germany
- Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Ina Kurth
- Division of Radiooncology/Radiobiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany
- German Cancer Consortium (DKTK), Core Center Heidelberg, Heidelberg, Germany
| | - Michael Baumann
- Division of Radiooncology/Radiobiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany
- German Cancer Consortium (DKTK), Core Center Heidelberg, Heidelberg, Germany
| | - Benedikt Brors
- Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany
- German Cancer Consortium (DKTK), Core Center Heidelberg, Heidelberg, Germany
- National Center for Tumor Diseases (NCT), Heidelberg, Germany
- Medical Faculty Heidelberg and Faculty of Biosciences, Heidelberg University, Heidelberg, Germany
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28
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Li Q, Lv H, Chen Y, Shen J, Shi J, Zhou C. Hybrid feature selection in a machine learning predictive model for perioperative myocardial injury in noncoronary cardiac surgery with cardiopulmonary bypass. Perfusion 2024:2676591241253459. [PMID: 38733257 DOI: 10.1177/02676591241253459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/13/2024]
Abstract
BACKGROUND Perioperative myocardial injury (PMI) is associated with increased mobility and mortality after noncoronary cardiac surgery. However, limited studies have developed a predictive model for PMI. Therefore, we used hybrid feature selection (FS) methods to establish a predictive model for PMI in noncoronary cardiac surgery with cardiopulmonary bypass (CPB). METHODS This was a single-center retrospective study conducted at the Fuwai Hospital in China. Patients aged 18-70 years who underwent elective noncoronary surgery with CPB at our institution from December 2018 to April 2021 were enrolled. The primary outcome was PMI, defined as the postoperative cardiac troponin I (cTnI) levels exceeding 220 times of upper reference limit (URL). Statistical analyses were conducted by Python (Python Software Foundation, version 3.9.7 and integrated development environment Jupyter Notebook 1.1.0) and SPSS software version 26.0 (IBM Corp., Armonk, New York, USA). RESULTS A total of 1130 patients were eventually eligible for this study. The incidence of PMI was 20.3% (229/1130) in the overall patients, 20.6% (163/791) in the training dataset, and 19.5% (66/339) in the testing dataset. The logistic regression model performed the best AUC of 0.6893 (95 CI%: 0.6371-0.7382) by the traditional selection method, and the random forest model performed the best AUC of 0.6937 (95 CI%: 0.6416-0.7423) by the union of Wrapper and Embedded method, and the CatBoost model performed the best AUC of 0.6828 (95 CI%: 0.6304-0.7320) by the union of Embedded and forward logistic regression technique, and the Naïve Bayes model achieved the best AUC with 0.7254 (95 CI%: 0.6746-0.7723) by forwarding logistic regression method. Moreover, the decision tree, KNeighborsClassifier, and support vector machine models performed the worse AUC in all selection forms. Furthermore, the SHapley Additive exPlanations plot showed that prolonged CPB, aortic clamp time, and preoperative low platelets count were strongly related to the PMI risk. CONCLUSIONS In total, four category feature selection methods were utilized, comprising five individual selection techniques and 15 combined methods. Notably, the combination of logistic regression and embedded methods demonstrated outstanding performance in predicting PMI risk. We also concluded that the machine learning model, including random forest, catboost, and Naive Bayes, were suitable candidates for establishing PMI predictive model. Nevertheless, additional investigation and validation are imperative for substantiating these finding.
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Affiliation(s)
- Qian Li
- Department of Anesthesiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Bejing, China
| | - Hong Lv
- Department of Anesthesiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Bejing, China
| | - Yuye Chen
- Department of Anesthesiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Bejing, China
| | - Jingjia Shen
- Department of Anesthesiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Bejing, China
| | - Jia Shi
- Department of Anesthesiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Bejing, China
| | - Chenghui Zhou
- Department of Anesthesiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Bejing, China
- Center for Anesthesiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
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29
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Genç M. Penalized logistic regression with prior information for microarray gene expression classification. Int J Biostat 2024; 20:107-122. [PMID: 36427223 DOI: 10.1515/ijb-2022-0025] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 11/07/2022] [Indexed: 02/17/2024]
Abstract
Cancer classification and gene selection are important applications in DNA microarray gene expression data analysis. Since DNA microarray data suffers from the high-dimensionality problem, automatic gene selection methods are used to enhance the classification performance of expert classifier systems. In this paper, a new penalized logistic regression method that performs simultaneous gene coefficient estimation and variable selection in DNA microarray data is discussed. The method employs prior information about the gene coefficients to improve the classification accuracy of the underlying model. The coordinate descent algorithm with screening rules is given to obtain the gene coefficient estimates of the proposed method efficiently. The performance of the method is examined on five high-dimensional cancer classification datasets using the area under the curve, the number of selected genes, misclassification rate and F-score measures. The real data analysis results indicate that the proposed method achieves a good cancer classification performance with a small misclassification rate, large area under the curve and F-score by trading off some sparsity level of the underlying model. Hence, the proposed method can be seen as a reliable penalized logistic regression method in the scope of high-dimensional cancer classification.
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Affiliation(s)
- Murat Genç
- Department of Management Information Systems, Faculty of Economics and Administrative Sciences, Tarsus University Mersin, Mersin 33400, Türkiye
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30
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Bai Z, Bartelo N, Aslam M, Murphy EA, Hale CR, Blachere NE, Parveen S, Spolaore E, DiCarlo E, Gravallese EM, Smith MH, Frank MO, Jiang CS, Zhang H, Pyrgaki C, Lewis MJ, Sikandar S, Pitzalis C, Lesnak JB, Mazhar K, Price TJ, Malfait AM, Miller RE, Zhang F, Goodman S, Darnell RB, Wang F, Orange DE. Synovial fibroblast gene expression is associated with sensory nerve growth and pain in rheumatoid arthritis. Sci Transl Med 2024; 16:eadk3506. [PMID: 38598614 DOI: 10.1126/scitranslmed.adk3506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 03/21/2024] [Indexed: 04/12/2024]
Abstract
It has been presumed that rheumatoid arthritis (RA) joint pain is related to inflammation in the synovium; however, recent studies reveal that pain scores in patients do not correlate with synovial inflammation. We developed a machine-learning approach (graph-based gene expression module identification or GbGMI) to identify an 815-gene expression module associated with pain in synovial biopsy samples from patients with established RA who had limited synovial inflammation at arthroplasty. We then validated this finding in an independent cohort of synovial biopsy samples from patients who had early untreated RA with little inflammation. Single-cell RNA sequencing analyses indicated that most of these 815 genes were most robustly expressed by lining layer synovial fibroblasts. Receptor-ligand interaction analysis predicted cross-talk between human lining layer fibroblasts and human dorsal root ganglion neurons expressing calcitonin gene-related peptide (CGRP+). Both RA synovial fibroblast culture supernatant and netrin-4, which is abundantly expressed by lining fibroblasts and was within the GbGMI-identified pain-associated gene module, increased the branching of pain-sensitive murine CGRP+ dorsal root ganglion neurons in vitro. Imaging of solvent-cleared synovial tissue with little inflammation from humans with RA revealed CGRP+ pain-sensing neurons encasing blood vessels growing into synovial hypertrophic papilla. Together, these findings support a model whereby synovial lining fibroblasts express genes associated with pain that enhance the growth of pain-sensing neurons into regions of synovial hypertrophy in RA.
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Affiliation(s)
- Zilong Bai
- Weill Cornell Medicine, New York, NY 10065, USA
| | | | | | | | - Caryn R Hale
- Rockefeller University, New York, NY 10065, USA
- Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Nathalie E Blachere
- Rockefeller University, New York, NY 10065, USA
- Howard Hughes Medical Institute, Rockefeller University, New York, NY 10065, USA
| | | | | | | | | | | | | | | | | | | | - Myles J Lewis
- Queen Mary University of London & NIHR BRC Barts Health NHS Trust, London E1 4NS, UK
| | - Shafaq Sikandar
- Queen Mary University of London & NIHR BRC Barts Health NHS Trust, London E1 4NS, UK
| | - Costantino Pitzalis
- Queen Mary University of London & NIHR BRC Barts Health NHS Trust, London E1 4NS, UK
- Department of Biomedical Sciences, Humanitas University & IRCC Humanitas Research Hospital, Milan 20072, Italy
| | | | | | | | | | | | - Fan Zhang
- University of Colorado School of Medicine, Aurora, CO 80045, USA
| | - Susan Goodman
- Hospital for Special Surgery, New York, NY 10021, USA
| | - Robert B Darnell
- Rockefeller University, New York, NY 10065, USA
- Howard Hughes Medical Institute, Rockefeller University, New York, NY 10065, USA
| | - Fei Wang
- Weill Cornell Medicine, New York, NY 10065, USA
| | - Dana E Orange
- Rockefeller University, New York, NY 10065, USA
- Hospital for Special Surgery, New York, NY 10021, USA
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Peruzzo D, Ciceri T, Mascheretti S, Lampis V, Arrigoni F, Agarwal N, Giubergia A, Villa FM, Crippa A, Nobile M, Mani E, Russo A, D'Angelo MG. Brain Alteration Patterns in Children with Duchenne Muscular Dystrophy: A Machine Learning Approach to Magnetic Resonance Imaging. J Neuromuscul Dis 2024:JND230075. [PMID: 38578898 DOI: 10.3233/jnd-230075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/07/2024]
Abstract
Background Duchenne Muscular Dystrophy (DMD) is a genetic disease in which lack of the dystrophin protein causes progressive muscular weakness, cardiomyopathy and respiratory insufficiency. DMD is often associated with other cognitive and behavioral impairments, however the correlation of abnormal dystrophin expression in the central nervous system with brain structure and functioning remains still unclear. Objective To investigate brain involvement in patients with DMD through a multimodal and multivariate approach accounting for potential comorbidities. Methods We acquired T1-weighted and Diffusion Tensor Imaging data from 18 patients with DMD and 18 age- and sex-matched controls with similar cognitive and behavioral profiles. Cortical thickness, structure volume, fractional anisotropy and mean diffusivity measures were used in a multivariate analysis performed using a Support Vector Machine classifier accounting for potential comorbidities in patients and controls. Results the classification experiment significantly discriminates between the two populations (97.2% accuracy) and the forward model weights showed that DMD mostly affects the microstructural integrity of long fiber bundles, in particular in the cerebellar peduncles (bilaterally), in the posterior thalamic radiation (bilaterally), in the fornix and in the medial lemniscus (bilaterally). We also reported a reduced cortical thickness, mainly in the motor cortex, cingulate cortex, hippocampal area and insula. Conclusions Our study identified a small pattern of alterations in the CNS likely associated with the DMD diagnosis.
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Affiliation(s)
- Denis Peruzzo
- Neuroimaging Unit,Scientific Institute, IRCCS Eugenio Medea, Bosisio Parini, Italy
| | - Tommaso Ciceri
- Neuroimaging Unit,Scientific Institute, IRCCS Eugenio Medea, Bosisio Parini, Italy
| | - Sara Mascheretti
- Child Psychopathology Unit,Scientific Institute, IRCCS Eugenio Medea, Bosisio Parini, Italy
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia (PV), Italy
| | - Valentina Lampis
- Child Psychopathology Unit,Scientific Institute, IRCCS Eugenio Medea, Bosisio Parini, Italy
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia (PV), Italy
| | - Filippo Arrigoni
- Neuroimaging Unit,Scientific Institute, IRCCS Eugenio Medea, Bosisio Parini, Italy
- Paediatric Radiology and Neuroradiology Department, V. Buzzi Children's Hospital, Milan, Italy
| | - Nivedita Agarwal
- Diagnostic Imaging and Neuroradiology Unit, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy
| | - Alice Giubergia
- Neuroimaging Unit,Scientific Institute, IRCCS Eugenio Medea, Bosisio Parini, Italy
| | - Filippo Maria Villa
- Child Psychopathology Unit,Scientific Institute, IRCCS Eugenio Medea, Bosisio Parini, Italy
| | - Alessandro Crippa
- Child Psychopathology Unit,Scientific Institute, IRCCS Eugenio Medea, Bosisio Parini, Italy
| | - Maria Nobile
- Child Psychopathology Unit,Scientific Institute, IRCCS Eugenio Medea, Bosisio Parini, Italy
| | - Elisa Mani
- Child Psychopathology Unit,Scientific Institute, IRCCS Eugenio Medea, Bosisio Parini, Italy
| | - Annamaria Russo
- Unit of Rehabilitation of Rare Diseases of the Central and Peripheral Nervous System, Scientific Institute, IRCCS Eugenio Medea, Bosisio Parini, Italy
| | - Maria Grazia D'Angelo
- Unit of Rehabilitation of Rare Diseases of the Central and Peripheral Nervous System, Scientific Institute, IRCCS Eugenio Medea, Bosisio Parini, Italy
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Choo SM, Sartori D, Lee SC, Yang HC, Syed-Abdul S. Data-Driven Identification of Factors That Influence the Quality of Adverse Event Reports: 15-Year Interpretable Machine Learning and Time-Series Analyses of VigiBase and QUEST. JMIR Med Inform 2024; 12:e49643. [PMID: 38568722 PMCID: PMC11024759 DOI: 10.2196/49643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 10/10/2023] [Accepted: 02/24/2024] [Indexed: 04/20/2024] Open
Abstract
BACKGROUND The completeness of adverse event (AE) reports, crucial for assessing putative causal relationships, is measured using the vigiGrade completeness score in VigiBase, the World Health Organization global database of reported potential AEs. Malaysian reports have surpassed the global average score (approximately 0.44), achieving a 5-year average of 0.79 (SD 0.23) as of 2019 and approaching the benchmark for well-documented reports (0.80). However, the contributing factors to this relatively high report completeness score remain unexplored. OBJECTIVE This study aims to explore the main drivers influencing the completeness of Malaysian AE reports in VigiBase over a 15-year period using vigiGrade. A secondary objective was to understand the strategic measures taken by the Malaysian authorities leading to enhanced report completeness across different time frames. METHODS We analyzed 132,738 Malaysian reports (2005-2019) recorded in VigiBase up to February 2021 split into historical International Drug Information System (INTDIS; n=63,943, 48.17% in 2005-2016) and newer E2B (n=68,795, 51.83% in 2015-2019) format subsets. For machine learning analyses, we performed a 2-stage feature selection followed by a random forest classifier to identify the top features predicting well-documented reports. We subsequently applied tree Shapley additive explanations to examine the magnitude, prevalence, and direction of feature effects. In addition, we conducted time-series analyses to evaluate chronological trends and potential influences of key interventions on reporting quality. RESULTS Among the analyzed reports, 42.84% (56,877/132,738) were well documented, with an increase of 65.37% (53,929/82,497) since 2015. Over two-thirds (46,186/68,795, 67.14%) of the Malaysian E2B reports were well documented compared to INTDIS reports at 16.72% (10,691/63,943). For INTDIS reports, higher pharmacovigilance center staffing was the primary feature positively associated with being well documented. In recent E2B reports, the top positive features included reaction abated upon drug dechallenge, reaction onset or drug use duration of <1 week, dosing interval of <1 day, reports from public specialist hospitals, reports by pharmacists, and reaction duration between 1 and 6 days. In contrast, reports from product registration holders and other health care professionals and reactions involving product substitution issues negatively affected the quality of E2B reports. Multifaceted strategies and interventions comprising policy changes, continuity of education, and human resource development laid the groundwork for AE reporting in Malaysia, whereas advancements in technological infrastructure, pharmacovigilance databases, and reporting tools concurred with increases in both the quantity and quality of AE reports. CONCLUSIONS Through interpretable machine learning and time-series analyses, this study identified key features that positively or negatively influence the completeness of Malaysian AE reports and unveiled how Malaysia has developed its pharmacovigilance capacity via multifaceted strategies and interventions. These findings will guide future work in enhancing pharmacovigilance and public health.
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Affiliation(s)
- Sim Mei Choo
- Centre of Compliance & Quality Control, National Pharmaceutical Regulatory Agency, Petaling Jaya, Malaysia
- Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei, Taiwan
| | | | - Sing Chet Lee
- Centre of Compliance & Quality Control, National Pharmaceutical Regulatory Agency, Petaling Jaya, Malaysia
| | - Hsuan-Chia Yang
- Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei, Taiwan
- International Center for Health Information Technology, Taipei Medical University, Taipei, Taiwan
- Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei Medical University, Taipei, Taiwan
- Research Center of Big Data and Meta-Analysis, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
| | - Shabbir Syed-Abdul
- Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei, Taiwan
- International Center for Health Information Technology, Taipei Medical University, Taipei, Taiwan
- School of Gerontology and Long-Term Care, Taipei Medical University, Taipei, Taiwan
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Herbst K, Wang T, Forchielli EJ, Thommes M, Paschalidis IC, Segrè D. Multi-Attribute Subset Selection enables prediction of representative phenotypes across microbial populations. Commun Biol 2024; 7:407. [PMID: 38570615 PMCID: PMC10991586 DOI: 10.1038/s42003-024-06093-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 03/22/2024] [Indexed: 04/05/2024] Open
Abstract
The interpretation of complex biological datasets requires the identification of representative variables that describe the data without critical information loss. This is particularly important in the analysis of large phenotypic datasets (phenomics). Here we introduce Multi-Attribute Subset Selection (MASS), an algorithm which separates a matrix of phenotypes (e.g., yield across microbial species and environmental conditions) into predictor and response sets of conditions. Using mixed integer linear programming, MASS expresses the response conditions as a linear combination of the predictor conditions, while simultaneously searching for the optimally descriptive set of predictors. We apply the algorithm to three microbial datasets and identify environmental conditions that predict phenotypes under other conditions, providing biologically interpretable axes for strain discrimination. MASS could be used to reduce the number of experiments needed to identify species or to map their metabolic capabilities. The generality of the algorithm allows addressing subset selection problems in areas beyond biology.
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Affiliation(s)
- Konrad Herbst
- Bioinformatics Program, Boston University, Boston, MA, USA
- Biological Design Center, Boston University, Boston, MA, USA
| | - Taiyao Wang
- Division of Systems Engineering, Boston University, Boston, MA, USA
| | - Elena J Forchielli
- Biological Design Center, Boston University, Boston, MA, USA
- Department of Biology, Boston University, Boston, MA, USA
| | - Meghan Thommes
- Biological Design Center, Boston University, Boston, MA, USA
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Ioannis Ch Paschalidis
- Division of Systems Engineering, Boston University, Boston, MA, USA.
- Department of Biomedical Engineering, Boston University, Boston, MA, USA.
- Faculty of Computing and Data Science, Boston University, Boston, MA, USA.
- Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA.
| | - Daniel Segrè
- Bioinformatics Program, Boston University, Boston, MA, USA.
- Biological Design Center, Boston University, Boston, MA, USA.
- Department of Biology, Boston University, Boston, MA, USA.
- Department of Biomedical Engineering, Boston University, Boston, MA, USA.
- Faculty of Computing and Data Science, Boston University, Boston, MA, USA.
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Gholizadeh M, Saeedi R, Bagheri A, Paeezi M. Machine learning-based prediction of effluent total suspended solids in a wastewater treatment plant using different feature selection approaches: A comparative study. ENVIRONMENTAL RESEARCH 2024; 246:118146. [PMID: 38215928 DOI: 10.1016/j.envres.2024.118146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 12/31/2023] [Accepted: 01/05/2024] [Indexed: 01/14/2024]
Abstract
Accurately predicting the characteristics of effluent, discharged from wastewater treatment plants (WWTPs) is crucial for reducing sampling requirements, labor, costs, and environmental pollution. Machine learning (ML) techniques can be effective in achieving this goal. To optimize ML-based models, various feature selection (FS) methods are employed. This study aims to investigate the impact of six FS methods (categorized as Wrapper, Filter, and Embedded methods) on the accuracy of three supervised ML algorithms in predicting total suspended solids (TSS) concentration in the effluent of a municipal wastewater treatment plant. Based on the features proposed by each FS method, five distinct scenarios were defined. Within each scenario, three ML algorithms, namely artificial neural network-multi layer perceptron (ANN-MLP), K-nearest neighbors (KNN), and adaptive boosting (AdaBoost) were applied. The features utilized for predicting TSS concentration in the WWTP effluent included BOD5, COD, TSS, TN, NH3 in the influent, and BOD5, COD, residual Cl2, NO3, TN, NH4 in the effluent. To construct the models, the dataset was randomly divided into training and testing subsets, and K-fold cross-validation was employed to control overfitting and underfitting. The evaluation metrics that are used are root mean squared error (RMSE), mean absolute error (MAE), and correlation coefficient (R2). The most efficient scenario was identified as Scenario IV, with the Sequential Backward Selection FS method. The features selected by this method were CODe, BOD5e, BOD5i, TNi. Furthermore, the ANN-MLP algorithm demonstrated the best performance, achieving the highest R2 value. This algorithm exhibited acceptable performance in both the training and testing subsets (R2 = 0.78 and R2 = 0.8, respectively).
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Affiliation(s)
- Mahdi Gholizadeh
- Environmental and Occupational Hazards Control Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Department of Health, Safety and Environment, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Reza Saeedi
- Department of Health, Safety and Environment, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Workplace Health Promotion Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Amin Bagheri
- Environmental and Occupational Hazards Control Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Department of Health, Safety and Environment, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Mohammad Paeezi
- Department of Health, Safety and Environment, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Workplace Health Promotion Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Eshel YD, Sharaha U, Beck G, Cohen-Logasi G, Lapidot I, Huleihel M, Mordechai S, Kapelushnik J, Salman A. Monitoring the efficacy of antibiotic therapy in febrile pediatric oncology patients with bacteremia using infrared spectroscopy of white blood cells-based machine learning. Talanta 2024; 270:125619. [PMID: 38199122 DOI: 10.1016/j.talanta.2023.125619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 12/29/2023] [Accepted: 12/30/2023] [Indexed: 01/12/2024]
Abstract
Bacteremia refers to the presence of bacteria in the bloodstream, which can lead to a serious and potentially life-threatening condition. In oncology patients, individuals undergoing cancer treatment have a higher risk of developing bacteremia due to a weakened immune system resulting from the disease itself or the treatments they receive. Prompt and accurate detection of bacterial infections and monitoring the effectiveness of antibiotic therapy are essential for enhancing patient outcomes and preventing the development and dissemination of multidrug-resistant bacteria. Traditional methods of infection monitoring, such as blood cultures and clinical observations, are time-consuming, labor-intensive, and often subject to limitations. This manuscript presents an innovative application of infrared spectroscopy of leucocytes of pediatric oncology patients with bacteremia combined with machine learning to diagnose the etiology of infection as bacterial and simultaneously monitor the efficacy of the antibiotic therapy in febrile pediatric oncology patients with bacteremia infections. Through the implementation of effective monitoring, it becomes possible to promptly identify any indications of treatment failure. This, in turn, indirectly serves to limit the progression of antibiotic resistance. The logistic regression (LR) classifier was able to differentiate the samples as bacterial or control within an hour, after receiving the blood samples with a success rate of over 95 %. Additionally, initial findings indicate that employing infrared spectroscopy of white blood cells (WBCs) along with machine learning is viable for monitoring the success of antibiotic therapy. Our follow up results demonstrate an accuracy of 87.5 % in assessing the effectiveness of the antibiotic treatment.
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Affiliation(s)
- Yotam D Eshel
- Department of Hematology and Oncology, Saban Pediatric Medical Center Soroka University Medical Center and Faculty of Health Sciences, Beer-Sheva, 84105, Israel
| | - Uraib Sharaha
- Department of Microbiology, Immunology, and Genetics, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, 84105, Israel; Department of Biology, Science and Technology College, Hebron University, Hebron, P760, Palestine
| | - Guy Beck
- Department of Hematology and Oncology, Saban Pediatric Medical Center Soroka University Medical Center and Faculty of Health Sciences, Beer-Sheva, 84105, Israel
| | - Gal Cohen-Logasi
- Department of Green Engineering, SCE-Sami Shamoon College of Engineering, Beer-Sheva, 84100, Israel
| | - Itshak Lapidot
- Department of Electrical and Electronics Engineering, ACLP-Afeka Center for Language Processing, Afeka Tel-Aviv Academic College of Engineering, Tel-Aviv, 69107, Israel; LIA Avignon Université, 339 Chemin des Meinajaries, Avignon, 84000, France
| | - Mahmoud Huleihel
- Department of Microbiology, Immunology, and Genetics, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, 84105, Israel
| | - Shaul Mordechai
- Department of Physics, Ben-Gurion University, Beer-Sheva, 84105, Israel
| | - Joseph Kapelushnik
- Department of Hematology and Oncology, Saban Pediatric Medical Center Soroka University Medical Center and Faculty of Health Sciences, Beer-Sheva, 84105, Israel
| | - Ahmad Salman
- Department of Physics, SCE-Sami Shamoon College of Engineering, Beer-Sheva, 84100, Israel.
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Tang B, Liu S, Feng X, Li C, Huo H, Wang A, Deng X, Yang C. Intelligent assessment of atrial fibrillation gradation based on sinus rhythm electrocardiogram and baseline information. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 247:108093. [PMID: 38401509 DOI: 10.1016/j.cmpb.2024.108093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 02/16/2024] [Accepted: 02/17/2024] [Indexed: 02/26/2024]
Abstract
BACKGROUND Atrial fibrillation (AF) is a progressive arrhythmia that significantly affects a patient's quality of life. The 4S-AF scheme is clinically recommended for AF management; however, the evaluation process is complex and time-consuming. This renders its promotion in primary medical institutions challenging. This retrospective study aimed to simplify the evaluation process and present an objective assessment model for AF gradation. METHODS In total, 189 12-lead electrocardiogram (ECG) recordings from 64 patients were included in this study. The data were annotated into two groups (mild and severe) according to the 4S-AF scheme. Using a preprocessed ECG during the sinus rhythm (SR), we obtained a synthesized vectorcardiogram (VCG). Subsequently, various features were calculated from both signals, and age, sex, and medical history were included as baseline characteristics. Different machine learning models, including support vector machines, random forests (RF), and logistic regression, were finally tested with a combination of feature selection techniques. RESULTS The proposed method demonstrated excellent performance in the classification of AF gradation. With an optimized feature set of VCG and baseline features, the RF model achieved accuracy, sensitivity, and specificity of 83.02 %, 80.56 %, and 88.24 %, respectively, under the inter-patient paradigm. CONCLUSION Our results demonstrate the value of physiological signals in AF gradation evaluation, and VCG signals were effective in identifying mild and severe AF. Considering its low computational complexity and high assessment performance, the proposed model is expected to serve as a useful prognostic tool for clinical AF management.
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Affiliation(s)
- Biqi Tang
- Department of Biomedical Engineering, School of Information Science and Technology, Fudan University, Shanghai, 200433, PR China
| | - Sen Liu
- Department of Biomedical Engineering, School of Information Science and Technology, Fudan University, Shanghai, 200433, PR China
| | - Xujian Feng
- Department of Biomedical Engineering, School of Information Science and Technology, Fudan University, Shanghai, 200433, PR China
| | - Chunpu Li
- Department of Cardiology, Xinghua City People's Hospital, Jiangsu, 225700, PR China
| | - Hongye Huo
- Department of Cardiology, Xinghua City People's Hospital, Jiangsu, 225700, PR China
| | - Aiguo Wang
- Department of Cardiology, Xinghua City People's Hospital, Jiangsu, 225700, PR China
| | - Xintao Deng
- Department of Cardiology, Xinghua City People's Hospital, Jiangsu, 225700, PR China.
| | - Cuiwei Yang
- Department of Biomedical Engineering, School of Information Science and Technology, Fudan University, Shanghai, 200433, PR China; Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai, 200093, PR China.
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Roy S, Sheikh SZ, Furey TS. CoVar: A generalizable machine learning approach to identify the coordinated regulators driving variational gene expression. PLoS Comput Biol 2024; 20:e1012016. [PMID: 38630807 PMCID: PMC11057768 DOI: 10.1371/journal.pcbi.1012016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 04/29/2024] [Accepted: 03/22/2024] [Indexed: 04/19/2024] Open
Abstract
Network inference is used to model transcriptional, signaling, and metabolic interactions among genes, proteins, and metabolites that identify biological pathways influencing disease pathogenesis. Advances in machine learning (ML)-based inference models exhibit the predictive capabilities of capturing latent patterns in genomic data. Such models are emerging as an alternative to the statistical models identifying causative factors driving complex diseases. We present CoVar, an ML-based framework that builds upon the properties of existing inference models, to find the central genes driving perturbed gene expression across biological states. Unlike differentially expressed genes (DEGs) that capture changes in individual gene expression across conditions, CoVar focuses on identifying variational genes that undergo changes in their expression network interaction profiles, providing insights into changes in the regulatory dynamics, such as in disease pathogenesis. Subsequently, it finds core genes from among the nearest neighbors of these variational genes, which are central to the variational activity and influence the coordinated regulatory processes underlying the observed changes in gene expression. Through the analysis of simulated as well as yeast expression data perturbed by the deletion of the mitochondrial genome, we show that CoVar captures the intrinsic variationality and modularity in the expression data, identifying key driver genes not found through existing differential analysis methodologies.
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Affiliation(s)
- Satyaki Roy
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Shehzad Z. Sheikh
- Departments of Medicine and Genetics, Center for Gastrointestinal Biology and Disease, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Terrence S. Furey
- Departments of Genetics and Biology, Center for Gastrointestinal Biology and Disease, University of North Carolina, Chapel Hill, North Carolina, United States of America
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Amoruso L, García AM, Pusil S, Timofeeva P, Quiñones I, Carreiras M. Decoding bilingualism from resting-state oscillatory network organization. Ann N Y Acad Sci 2024; 1534:106-117. [PMID: 38419368 DOI: 10.1111/nyas.15113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/02/2024]
Abstract
Can lifelong bilingualism be robustly decoded from intrinsic brain connectivity? Can we determine, using a spectrally resolved approach, the oscillatory networks that better predict dual-language experience? We recorded resting-state magnetoencephalographic activity in highly proficient Spanish-Basque bilinguals and Spanish monolinguals, calculated functional connectivity at canonical frequency bands, and derived topological network properties using graph analysis. These features were fed into a machine learning classifier to establish how robustly they discriminated between the groups. The model showed excellent classification (AUC: 0.91 ± 0.12) between individuals in each group. The key drivers of classification were network strength in beta (15-30 Hz) and delta (2-4 Hz) rhythms. Further characterization of these networks revealed the involvement of temporal, cingulate, and fronto-parietal hubs likely underpinning the language and default-mode networks (DMNs). Complementary evidence from a correlation analysis showed that the top-ranked features that better discriminated individuals during rest also explained interindividual variability in second language (L2) proficiency within bilinguals, further supporting the robustness of the machine learning model in capturing trait-like markers of bilingualism. Overall, our results show that long-term experience with an L2 can be "brain-read" at a fine-grained level from resting-state oscillatory network organization, highlighting its pervasive impact, particularly within language and DMN networks.
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Affiliation(s)
- Lucia Amoruso
- Basque Center on Cognition, Brain and Language (BCBL), San Sebastian, Spain
- Ikerbasque, Basque Foundation for Science, Bilbao, Spain
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina
| | - Adolfo M García
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina
- Global Brain Health Institute, University of California San Francisco, San Francisco, California, USA
- Departamento de Lingüística y Literatura, Facultad de Humanidades, Universidad de Santiago de Chile, Santiago, Chile
| | - Sandra Pusil
- Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain
| | - Polina Timofeeva
- Basque Center on Cognition, Brain and Language (BCBL), San Sebastian, Spain
- Universidad del País Vasco (UPV/EHU), San Sebastian, Spain
| | - Ileana Quiñones
- Basque Center on Cognition, Brain and Language (BCBL), San Sebastian, Spain
- Ikerbasque, Basque Foundation for Science, Bilbao, Spain
| | - Manuel Carreiras
- Basque Center on Cognition, Brain and Language (BCBL), San Sebastian, Spain
- Ikerbasque, Basque Foundation for Science, Bilbao, Spain
- Universidad del País Vasco (UPV/EHU), San Sebastian, Spain
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Tran PB, Nikolaidis GF, Abatih E, Bos P, Berete F, Gorasso V, Van der Heyden J, Kazibwe J, Tomeny EM, Van Hal G, Beutels P, van Olmen J. Multimorbidity healthcare expenditure in Belgium: a 4-year analysis (COMORB study). Health Res Policy Syst 2024; 22:35. [PMID: 38519938 PMCID: PMC10960468 DOI: 10.1186/s12961-024-01113-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 01/24/2024] [Indexed: 03/25/2024] Open
Abstract
BACKGROUND The complex management of health needs in multimorbid patients, alongside limited cost data, presents challenges in developing cost-effective patient-care pathways. We estimated the costs of managing 171 dyads and 969 triads in Belgium, taking into account the influence of morbidity interactions on costs. METHODS We followed a retrospective longitudinal study design, using the linked Belgian Health Interview Survey 2018 and the administrative claim database 2017-2020 hosted by the Intermutualistic Agency. We included people aged 15 and older, who had complete profiles (N = 9753). Applying a system costing perspective, the average annual direct cost per person per dyad/triad was presented in 2022 Euro and comprised mainly direct medical costs. We developed mixed models to analyse the impact of single chronic conditions, dyads and triads on healthcare costs, considering two-/three-way interactions within dyads/triads, key cost determinants and clustering at the household level. RESULTS People with multimorbidity constituted nearly half of the study population and their total healthcare cost constituted around three quarters of the healthcare cost of the study population. The most common dyad, arthropathies + dorsopathies, with a 14% prevalence rate, accounted for 11% of the total national health expenditure. The most frequent triad, arthropathies + dorsopathies + hypertension, with a 5% prevalence rate, contributed 5%. The average annual direct costs per person with dyad and triad were €3515 (95% CI 3093-3937) and €4592 (95% CI 3920-5264), respectively. Dyads and triads associated with cancer, diabetes, chronic fatigue, and genitourinary problems incurred the highest costs. In most cases, the cost associated with multimorbidity was lower or not substantially different from the combined cost of the same conditions observed in separate patients. CONCLUSION Prevalent morbidity combinations, rather than high-cost ones, made a greater contribution to total national health expenditure. Our study contributes to the sparse evidence on this topic globally and in Europe, with the aim of improving cost-effective care for patients with diverse needs.
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Affiliation(s)
- Phuong Bich Tran
- Department of Family Medicine and Population Health, University of Antwerp, Antwerp, Belgium.
- Department of Epidemiology and public health, Brussels, Belgium.
| | | | - Emmanuel Abatih
- Department of Applied Mathematics, Computer Sciences and Statistics, Ghent University, Ghent, Belgium
| | - Philippe Bos
- Department of Sociology, University of Antwerp, Antwerp, Belgium
| | - Finaba Berete
- Department of Epidemiology and public health, Brussels, Belgium
| | - Vanessa Gorasso
- Department of Epidemiology and public health, Brussels, Belgium
| | | | - Joseph Kazibwe
- Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Ewan Morgan Tomeny
- Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, UK
| | - Guido Van Hal
- Department of Family Medicine and Population Health, University of Antwerp, Antwerp, Belgium
| | - Philippe Beutels
- Centre for Health Economics Research & Modelling Infectious Diseases (CHERMID), University of Antwerp, Antwerp, Belgium
| | - Josefien van Olmen
- Department of Family Medicine and Population Health, University of Antwerp, Antwerp, Belgium
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Meeker KL, Luckett PH, Barthélemy NR, Hobbs DA, Chen C, Bollinger J, Ovod V, Flores S, Keefe S, Henson RL, Herries EM, McDade E, Hassenstab JJ, Xiong C, Cruchaga C, Benzinger TLS, Holtzman DM, Schindler SE, Bateman RJ, Morris JC, Gordon BA, Ances BM. Comparison of cerebrospinal fluid, plasma and neuroimaging biomarker utility in Alzheimer's disease. Brain Commun 2024; 6:fcae081. [PMID: 38505230 PMCID: PMC10950051 DOI: 10.1093/braincomms/fcae081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 02/01/2024] [Accepted: 03/14/2024] [Indexed: 03/21/2024] Open
Abstract
Alzheimer's disease biomarkers are crucial to understanding disease pathophysiology, aiding accurate diagnosis and identifying target treatments. Although the number of biomarkers continues to grow, the relative utility and uniqueness of each is poorly understood as prior work has typically calculated serial pairwise relationships on only a handful of markers at a time. The present study assessed the cross-sectional relationships among 27 Alzheimer's disease biomarkers simultaneously and determined their ability to predict meaningful clinical outcomes using machine learning. Data were obtained from 527 community-dwelling volunteers enrolled in studies at the Charles F. and Joanne Knight Alzheimer Disease Research Center at Washington University in St Louis. We used hierarchical clustering to group 27 imaging, CSF and plasma measures of amyloid beta, tau [phosphorylated tau (p-tau), total tau t-tau)], neuronal injury and inflammation drawn from MRI, PET, mass-spectrometry assays and immunoassays. Neuropsychological and genetic measures were also included. Random forest-based feature selection identified the strongest predictors of amyloid PET positivity across the entire cohort. Models also predicted cognitive impairment across the entire cohort and in amyloid PET-positive individuals. Four clusters emerged reflecting: core Alzheimer's disease pathology (amyloid and tau), neurodegeneration, AT8 antibody-associated phosphorylated tau sites and neuronal dysfunction. In the entire cohort, CSF p-tau181/Aβ40lumi and Aβ42/Aβ40lumi and mass spectrometry measurements for CSF pT217/T217, pT111/T111, pT231/T231 were the strongest predictors of amyloid PET status. Given their ability to denote individuals on an Alzheimer's disease pathological trajectory, these same markers (CSF pT217/T217, pT111/T111, p-tau/Aβ40lumi and t-tau/Aβ40lumi) were largely the best predictors of worse cognition in the entire cohort. When restricting analyses to amyloid-positive individuals, the strongest predictors of impaired cognition were tau PET, CSF t-tau/Aβ40lumi, p-tau181/Aβ40lumi, CSF pT217/217 and pT205/T205. Non-specific CSF measures of neuronal dysfunction and inflammation were poor predictors of amyloid PET and cognitive status. The current work utilized machine learning to understand the interrelationship structure and utility of a large number of biomarkers. The results demonstrate that, although the number of biomarkers has rapidly expanded, many are interrelated and few strongly predict clinical outcomes. Examining the entire corpus of available biomarkers simultaneously provides a meaningful framework to understand Alzheimer's disease pathobiological change as well as insight into which biomarkers may be most useful in Alzheimer's disease clinical practice and trials.
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Affiliation(s)
- Karin L Meeker
- Department of Neurology, Washington University in St Louis, St Louis, MO 63110, USA
| | - Patrick H Luckett
- Department of Neurosurgery, Washington University in St Louis, St Louis, MO 63110, USA
| | - Nicolas R Barthélemy
- Department of Neurology, Washington University in St Louis, St Louis, MO 63110, USA
| | - Diana A Hobbs
- Department of Radiology, Washington University in St Louis, St Louis, MO 63110, USA
| | - Charles Chen
- Department of Radiology, Washington University in St Louis, St Louis, MO 63110, USA
| | - James Bollinger
- Department of Neurology, Washington University in St Louis, St Louis, MO 63110, USA
| | - Vitaliy Ovod
- Department of Neurology, Washington University in St Louis, St Louis, MO 63110, USA
| | - Shaney Flores
- Department of Radiology, Washington University in St Louis, St Louis, MO 63110, USA
| | - Sarah Keefe
- Department of Radiology, Washington University in St Louis, St Louis, MO 63110, USA
| | - Rachel L Henson
- Department of Neurology, Washington University in St Louis, St Louis, MO 63110, USA
| | - Elizabeth M Herries
- Department of Neurology, Washington University in St Louis, St Louis, MO 63110, USA
| | - Eric McDade
- Department of Neurology, Washington University in St Louis, St Louis, MO 63110, USA
| | - Jason J Hassenstab
- Department of Neurology, Washington University in St Louis, St Louis, MO 63110, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St Louis, MO 63110, USA
| | - Chengjie Xiong
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St Louis, MO 63110, USA
- Division of Biostatistics, Washington University in St Louis, St Louis, MO 63110, USA
| | - Carlos Cruchaga
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St Louis, MO 63110, USA
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO 63110, USA
| | - Tammie L S Benzinger
- Department of Radiology, Washington University in St Louis, St Louis, MO 63110, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St Louis, MO 63110, USA
| | - David M Holtzman
- Department of Neurology, Washington University in St Louis, St Louis, MO 63110, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St Louis, MO 63110, USA
| | - Suzanne E Schindler
- Department of Neurology, Washington University in St Louis, St Louis, MO 63110, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St Louis, MO 63110, USA
| | - Randall J Bateman
- Department of Neurology, Washington University in St Louis, St Louis, MO 63110, USA
| | - John C Morris
- Department of Neurology, Washington University in St Louis, St Louis, MO 63110, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St Louis, MO 63110, USA
| | - Brian A Gordon
- Department of Radiology, Washington University in St Louis, St Louis, MO 63110, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St Louis, MO 63110, USA
| | - Beau M Ances
- Department of Neurology, Washington University in St Louis, St Louis, MO 63110, USA
- Department of Radiology, Washington University in St Louis, St Louis, MO 63110, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St Louis, MO 63110, USA
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Tiwari AK, Saini R, Nath A, Singh P, Shah MA. Hybrid similarity relation based mutual information for feature selection in intuitionistic fuzzy rough framework and its applications. Sci Rep 2024; 14:5958. [PMID: 38472266 PMCID: PMC10933482 DOI: 10.1038/s41598-024-55902-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Accepted: 02/28/2024] [Indexed: 03/14/2024] Open
Abstract
Fuzzy rough entropy established in the notion of fuzzy rough set theory, which has been effectively and efficiently applied for feature selection to handle the uncertainty in real-valued datasets. Further, Fuzzy rough mutual information has been presented by integrating information entropy with fuzzy rough set to measure the importance of features. However, none of the methods till date can handle noise, uncertainty and vagueness simultaneously due to both judgement and identification, which lead to degrade the overall performances of the learning algorithms with the increment in the number of mixed valued conditional features. In the current study, these issues are tackled by presenting a novel intuitionistic fuzzy (IF) assisted mutual information concept along with IF granular structure. Initially, a hybrid IF similarity relation is introduced. Based on this relation, an IF granular structure is introduced. Then, IF rough conditional and joint entropies are established. Further, mutual information based on these concepts are discussed. Next, mathematical theorems are proved to demonstrate the validity of the given notions. Thereafter, significance of the features subset is computed by using this mutual information, and corresponding feature selection is suggested to delete the irrelevant and redundant features. The current approach effectively handles noise and subsequent uncertainty in both nominal and mixed data (including both nominal and category variables). Moreover, comprehensive experimental performances are evaluated on real-valued benchmark datasets to demonstrate the practical validation and effectiveness of the addressed technique. Finally, an application of the proposed method is exhibited to improve the prediction of phospholipidosis positive molecules. RF(h2o) produces the most effective results till date based on our proposed methodology with sensitivity, accuracy, specificity, MCC, and AUC of 86.7%, 90.1%, 93.0% , 0.808, and 0.922 respectively.
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Affiliation(s)
- Anoop Kumar Tiwari
- Department of Computer Science and Information Technology, Central University of Haryana, Mahendergarh, 123031, India
| | - Rajat Saini
- Department of Mathematics, School of Basic Sciences, Central University of Haryana, Mahendergarh, 123031, India.
| | - Abhigyan Nath
- Department of Biochemistry, Pt. Jawahar Lal Nehru Memorial Medical College, Raipur, 492001, India
| | - Phool Singh
- Department of Mathematics (SoET), Central University of Haryana, Mahendergarh, 123031, India
| | - Mohd Asif Shah
- Department of Economics, Kebri Dehar University, 250, Kebri Dehar, Somali, Ethiopia.
- Centre of Research Impact and Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, 140401, Punjab, India.
- Division of Research and Development, Lovely Professional University, Phagwara, 144001, Punjab, India.
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42
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Peng M, Lin B, Zhang J, Zhou Y, Lin B. scFSNN: a feature selection method based on neural network for single-cell RNA-seq data. BMC Genomics 2024; 25:264. [PMID: 38459442 PMCID: PMC10924397 DOI: 10.1186/s12864-024-10160-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 02/25/2024] [Indexed: 03/10/2024] Open
Abstract
While single-cell RNA sequencing (scRNA-seq) allows researchers to analyze gene expression in individual cells, its unique characteristics like over-dispersion, zero-inflation, high gene-gene correlation, and large data volume with many features pose challenges for most existing feature selection methods. In this paper, we present a feature selection method based on neural network (scFSNN) to solve classification problem for the scRNA-seq data. scFSNN is an embedded method that can automatically select features (genes) during model training, control the false discovery rate of selected features and adaptively determine the number of features to be eliminated. Extensive simulation and real data studies demonstrate its excellent feature selection ability and predictive performance.
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Affiliation(s)
- Minjiao Peng
- School of Mathematical Sciences, Shenzhen University, Nanshan, Shenzhen, 518060, Guangdong, China
- School of Mathematics and Statistics and KLAS, Northeast Normal University, Renmin Street, Changchun, 130000, Jilin, China
| | - Baoqin Lin
- Experimental Center, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, 510405, China
| | - Jun Zhang
- School of Mathematical Sciences, Shenzhen University, Nanshan, Shenzhen, 518060, Guangdong, China
| | - Yan Zhou
- School of Mathematical Sciences, Shenzhen University, Nanshan, Shenzhen, 518060, Guangdong, China
| | - Bingqing Lin
- School of Mathematical Sciences, Shenzhen University, Nanshan, Shenzhen, 518060, Guangdong, China.
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Aghakhani A, Yousefi M, Yekaninejad MS. Machine Learning Models for Predicting Sudden Sensorineural Hearing Loss Outcome: A Systematic Review. Ann Otol Rhinol Laryngol 2024; 133:268-276. [PMID: 37864312 DOI: 10.1177/00034894231206902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2023]
Abstract
BACKGROUND Machine Learning models have been applied in various healthcare fields, including Audiology, to predict disease outcomes. The prognosis of sudden sensorineural hearing loss is difficult to predict due to the variable course of the disease. Hence, researchers have attempted to utilize ML models to predict the outcome of patients with sudden sensorineural hearing loss. The objectives of this study were to review the performance of these machine learning models and assess their applicability in real-world settings. METHODS A systematic search was conducted in PubMed, Web of Science and Scopus. Only studies that built machine learning prediction models were included, and studies that used algorithms such as logistic regression only for the purpose of adjusting for confounding variables were excluded. The risk of bias was assessed using the Prediction model Risk of Bias Assessment Tool (PROBAST). RESULTS After screening, a total of 7 papers were eligible for synthesis. In total, these studies built 48 ML models. The most common utilized algorithms were Logistic Regression, Support Vector Machine (SVM) and boosting. The area under the curve of the receiver operating characteristic curve ranged between 0.59 and 0.915. All of the included studies had a high risk of bias; hence there are concerns regarding their applicability. CONCLUSION Although these models showed great performance and promising results, future studies are still needed before these models can be applied in a real-world setting. Future studies should employ multiple cohorts, different feature selection methods, and external validation to further validate the models' applicability.
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Affiliation(s)
- Amirhossein Aghakhani
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Milad Yousefi
- Department of Audiology, School of Rehabilitation, Tehran University of Medical Sciences, Tehran, Iran
| | - Mir Saeed Yekaninejad
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
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Liana A, Hałuszczuk A, Gawor A, Bulska E. Identification and Structural Characterization of Degradation Products of Linagliptin by Mass Spectrometry Techniques. Int J Mol Sci 2024; 25:2591. [PMID: 38473837 DOI: 10.3390/ijms25052591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 02/17/2024] [Accepted: 02/21/2024] [Indexed: 03/14/2024] Open
Abstract
As part of the development and production of pharmaceuticals, the purity of Active Pharmaceutical Ingredients stands as a fundamental parameter that significantly influences the quality, safety, and efficacy of the final drug product. Impurities in Active Pharmaceutical Ingredients are various unwanted substances that can appear during the whole manufacturing process, from raw materials to the final product. These impurities can stem from multiple sources, including starting materials, intermediates, reagents, solvents, and even degradation products resulting from exposure to environmental factors such as heat, light, or moisture. Their presence can potentially compromise the therapeutic effect of the drug, introduce unexpected side effects, or even pose safety risks to patients. This study aims to conduct the forced degradation of linagliptin and subsequently attempt to identify the resulting degradants. The degradation procedures were carried out in accordance with the guidelines of the International Committee for Harmonization. The degradation profile of linagliptin was investigated under various conditions, including acid hydrolysis, alkaline hydrolysis, oxidation, heat, and light exposure, utilizing ultra-performance liquid chromatography connected to a photo array detector. Identification and characterization of the degradation products were achieved using an ultra-performance liquid chromatography coupled with a single quadrupole detector mass spectrometer and also a liquid chromatography coupled with a high-resolution mass spectrometry. The identified degradation products demonstrate that linagliptin is particularly susceptible to degradation when exposed to acid and peroxide. Whereas, no significant degradation effects were observed under alkali, thermolytic, and photolytic conditions.
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Affiliation(s)
- Aleksandra Liana
- Biological and Chemical Research Centre, Faculty of Chemistry, University of Warsaw, Żwirki i Wigury 101, 02-089 Warsaw, Poland
- Pharmaceutical Plant Polpharma S.A., Pelpińska 19, 83-200 Starograd Gdański, Poland
| | - Adam Hałuszczuk
- Faculty of Chemistry, Gdańsk University of Technology, Gabriela Narutowicza 11/12, 80-233 Gdańsk, Poland
| | - Andrzej Gawor
- Biological and Chemical Research Centre, Faculty of Chemistry, University of Warsaw, Żwirki i Wigury 101, 02-089 Warsaw, Poland
| | - Ewa Bulska
- Biological and Chemical Research Centre, Faculty of Chemistry, University of Warsaw, Żwirki i Wigury 101, 02-089 Warsaw, Poland
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45
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Rodrigues J, Amin A, Chandra S, Mulla NJ, Nayak GS, Rai S, Ray S, Mahato KK. Machine Learning Enabled Photoacoustic Spectroscopy for Noninvasive Assessment of Breast Tumor Progression In Vivo: A Preclinical Study. ACS Sens 2024; 9:589-601. [PMID: 38288735 PMCID: PMC10897932 DOI: 10.1021/acssensors.3c01085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 11/25/2023] [Accepted: 01/17/2024] [Indexed: 02/24/2024]
Abstract
Breast cancer is a dreaded disease affecting women the most in cancer-related deaths over other cancers. However, early diagnosis of the disease can help increase survival rates. The existing breast cancer diagnosis tools do not support the early diagnosis of the disease. Therefore, there is a great need to develop early diagnostic tools for this cancer. Photoacoustic spectroscopy (PAS), being very sensitive to biochemical changes, can be relied upon for its application in detecting breast tumors in vivo. With this motivation, in the current study, an aseptic chamber integrated photoacoustic (PA) probe was designed and developed to monitor breast tumor progression in vivo, established in nude mice. The device served the dual purpose of transporting tumor-bearing animals to the laboratory from the animal house and performing PA experiments in the same chamber, maintaining sterility. In the current study, breast tumor was induced in the nude mice by MCF-7 cells injection and the corresponding PA spectra at different time points (day 0, 5, 10, 15, and 20) of tumor progression in vivo in the same animals. The recorded photoacoustic spectra were subsequently preprocessed, wavelet-transformed, and subjected to filter-based feature selection algorithm. The selected top 20 features, by minimum redundancy maximum relevance (mRMR) algorithm, were then used to build an input feature matrix for machine learning (ML)-based classification of the data. The performance of classification models demonstrated 100% specificity, whereas the sensitivity of 95, 100, 92.5, and 85% for the time points, day 5, 10, 15, and 20, respectively. These results suggest the potential of PA signal-based classification of breast tumor progression in a preclinical model. The PA signal contains information on the biochemical changes associated with disease progression, emphasizing its translational strength toward early disease diagnosis.
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Affiliation(s)
- Jackson Rodrigues
- Department
of Biophysics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Karnataka, Manipal 576104, India
| | - Ashwini Amin
- Department
of Computer Science and Engineering, Manipal
Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India
| | - Subhash Chandra
- Department
of Biophysics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Karnataka, Manipal 576104, India
| | - Nitufa J. Mulla
- Department
of Biophysics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Karnataka, Manipal 576104, India
| | - G. Subramanya Nayak
- Department
of Electronics and Communication, Manipal
Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India
| | - Sharada Rai
- Department
of Pathology, Kasturba Medical College Mangalore,
Manipal Academy of Higher Education, Karnataka, Manipal 576104, India
| | - Satadru Ray
- Department
of Surgery, Kasturba Medical College, Manipal
Academy of Higher Education, Karnataka,Manipal 576104, India
| | - Krishna Kishore Mahato
- Department
of Biophysics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Karnataka, Manipal 576104, India
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Quinn TP, Hess JL, Marshe VS, Barnett MM, Hauschild AC, Maciukiewicz M, Elsheikh SSM, Men X, Schwarz E, Trakadis YJ, Breen MS, Barnett EJ, Zhang-James Y, Ahsen ME, Cao H, Chen J, Hou J, Salekin A, Lin PI, Nicodemus KK, Meyer-Lindenberg A, Bichindaritz I, Faraone SV, Cairns MJ, Pandey G, Müller DJ, Glatt SJ. A primer on the use of machine learning to distil knowledge from data in biological psychiatry. Mol Psychiatry 2024; 29:387-401. [PMID: 38177352 PMCID: PMC11228968 DOI: 10.1038/s41380-023-02334-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 09/21/2023] [Accepted: 11/17/2023] [Indexed: 01/06/2024]
Abstract
Applications of machine learning in the biomedical sciences are growing rapidly. This growth has been spurred by diverse cross-institutional and interdisciplinary collaborations, public availability of large datasets, an increase in the accessibility of analytic routines, and the availability of powerful computing resources. With this increased access and exposure to machine learning comes a responsibility for education and a deeper understanding of its bases and bounds, borne equally by data scientists seeking to ply their analytic wares in medical research and by biomedical scientists seeking to harness such methods to glean knowledge from data. This article provides an accessible and critical review of machine learning for a biomedically informed audience, as well as its applications in psychiatry. The review covers definitions and expositions of commonly used machine learning methods, and historical trends of their use in psychiatry. We also provide a set of standards, namely Guidelines for REporting Machine Learning Investigations in Neuropsychiatry (GREMLIN), for designing and reporting studies that use machine learning as a primary data-analysis approach. Lastly, we propose the establishment of the Machine Learning in Psychiatry (MLPsych) Consortium, enumerate its objectives, and identify areas of opportunity for future applications of machine learning in biological psychiatry. This review serves as a cautiously optimistic primer on machine learning for those on the precipice as they prepare to dive into the field, either as methodological practitioners or well-informed consumers.
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Affiliation(s)
- Thomas P Quinn
- Applied Artificial Intelligence Institute (A2I2), Burwood, VIC, 3125, Australia
| | - Jonathan L Hess
- Department of Psychiatry and Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
| | - Victoria S Marshe
- Institute of Medical Science, University of Toronto, Toronto, ON, M5S 1A1, Canada
- Pharmacogenetics Research Clinic, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, M5S 1A1, Canada
| | - Michelle M Barnett
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, NSW, 2308, Australia
- Precision Medicine Research Program, Hunter Medical Research Institute, Newcastle, NSW, 2308, Australia
| | - Anne-Christin Hauschild
- Department of Medical Informatics, Medical University Center Göttingen, Göttingen, Lower Saxony, 37075, Germany
| | - Malgorzata Maciukiewicz
- Hospital Zurich, University of Zurich, Zurich, 8091, Switzerland
- Department of Rheumatology and Immunology, University Hospital Bern, Bern, 3010, Switzerland
- Department for Biomedical Research (DBMR), University of Bern, Bern, 3010, Switzerland
| | - Samar S M Elsheikh
- Pharmacogenetics Research Clinic, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, M5S 1A1, Canada
| | - Xiaoyu Men
- Pharmacogenetics Research Clinic, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, M5S 1A1, Canada
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, M5S 1A1, Canada
| | - Emanuel Schwarz
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Baden-Württemberg, J5 68159, Germany
| | - Yannis J Trakadis
- Department Human Genetics, McGill University Health Centre, Montreal, QC, H4A 3J1, Canada
| | - Michael S Breen
- Psychiatry, Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Eric J Barnett
- Department of Neuroscience and Physiology, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
| | - Yanli Zhang-James
- Department of Psychiatry and Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
| | - Mehmet Eren Ahsen
- Department of Business Administration, Gies College of Business, University of Illinois at Urbana-Champaign, Champaign, IL, 61820, USA
- Department of Biomedical and Translational Sciences, Carle-Illinois School of Medicine, University of Illinois at Urbana-Champaign, Champaign, IL, 61820, USA
| | - Han Cao
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Baden-Württemberg, J5 68159, Germany
| | - Junfang Chen
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Baden-Württemberg, J5 68159, Germany
| | - Jiahui Hou
- Department of Psychiatry and Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
- Department of Neuroscience and Physiology, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
| | - Asif Salekin
- Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY, 13244, USA
| | - Ping-I Lin
- Discipline of Psychiatry and Mental Health, University of New South Wales, Sydney, NSW, 2052, Australia
- Mental Health Research Unit, South Western Sydney Local Health District, Liverpool, NSW, 2170, Australia
| | | | - Andreas Meyer-Lindenberg
- Clinical Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Baden-Württemberg, J5 68159, Germany
| | - Isabelle Bichindaritz
- Biomedical and Health Informatics/Computer Science Department, State University of New York at Oswego, Oswego, NY, 13126, USA
- Intelligent Bio Systems Lab, State University of New York at Oswego, Oswego, NY, 13126, USA
| | - Stephen V Faraone
- Department of Psychiatry and Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
- Department of Neuroscience and Physiology, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
| | - Murray J Cairns
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, NSW, 2308, Australia
- Precision Medicine Research Program, Hunter Medical Research Institute, Newcastle, NSW, 2308, Australia
| | - Gaurav Pandey
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Daniel J Müller
- Pharmacogenetics Research Clinic, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, M5S 1A1, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, M5S 1A1, Canada
- Department of Psychiatry, Psychosomatics and Psychotherapy, Center of Mental Health, University Hospital of Würzburg, Würzburg, 97080, Germany
| | - Stephen J Glatt
- Department of Psychiatry and Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA.
- Department of Neuroscience and Physiology, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA.
- Department of Public Health and Preventive Medicine, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA.
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Fonseca A, Spytek M, Biecek P, Cordeiro C, Sepúlveda N. Antibody selection strategies and their impact in predicting clinical malaria based on multi-sera data. BioData Min 2024; 17:2. [PMID: 38273386 PMCID: PMC10811867 DOI: 10.1186/s13040-024-00354-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 01/16/2024] [Indexed: 01/27/2024] Open
Abstract
BACKGROUND Nowadays, the chance of discovering the best antibody candidates for predicting clinical malaria has notably increased due to the availability of multi-sera data. The analysis of these data is typically divided into a feature selection phase followed by a predictive one where several models are constructed for predicting the outcome of interest. A key question in the analysis is to determine which antibodies should be included in the predictive stage and whether they should be included in the original or a transformed scale (i.e. binary/dichotomized). METHODS To answer this question, we developed three approaches for antibody selection in the context of predicting clinical malaria: (i) a basic and simple approach based on selecting antibodies via the nonparametric Mann-Whitney-Wilcoxon test; (ii) an optimal dychotomizationdichotomization approach where each antibody was selected according to the optimal cut-off via maximization of the chi-squared (χ2) statistic for two-way tables; (iii) a hybrid parametric/non-parametric approach that integrates Box-Cox transformation followed by a t-test, together with the use of finite mixture models and the Mann-Whitney-Wilcoxon test as a last resort. We illustrated the application of these three approaches with published serological data of 36 Plasmodium falciparum antigens for predicting clinical malaria in 121 Kenyan children. The predictive analysis was based on a Super Learner where predictions from multiple classifiers including the Random Forest were pooled together. RESULTS Our results led to almost similar areas under the Receiver Operating Characteristic curves of 0.72 (95% CI = [0.62, 0.82]), 0.80 (95% CI = [0.71, 0.89]), 0.79 (95% CI = [0.7, 0.88]) for the simple, dichotomization and hybrid approaches, respectively. These approaches were based on 6, 20, and 16 antibodies, respectively. CONCLUSIONS The three feature selection strategies provided a better predictive performance of the outcome when compared to the previous results relying on Random Forest including all the 36 antibodies (AUC = 0.68, 95% CI = [0.57;0.79]). Given the similar predictive performance, we recommended that the three strategies should be used in conjunction in the same data set and selected according to their complexity.
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Affiliation(s)
- André Fonseca
- FCT - Faculdade de Ciências e Tecnologia, Universidade do Algarve, Faro, Portugal
- CEAUL - Centro de Estatística e Aplicações da Universidade de Lisboa, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal
| | - Mikolaj Spytek
- Faculty of Mathematics & Information Science, Warsaw University of Technology, Warsaw, Poland
| | - Przemysław Biecek
- Faculty of Mathematics & Information Science, Warsaw University of Technology, Warsaw, Poland
| | - Clara Cordeiro
- FCT - Faculdade de Ciências e Tecnologia, Universidade do Algarve, Faro, Portugal
- CEAUL - Centro de Estatística e Aplicações da Universidade de Lisboa, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal
| | - Nuno Sepúlveda
- CEAUL - Centro de Estatística e Aplicações da Universidade de Lisboa, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal.
- Faculty of Mathematics & Information Science, Warsaw University of Technology, Warsaw, Poland.
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Zhu T, Feng Y, Dong X, Yang X, Liu B, Yuan P, Song X, Chen S, Sui S. Optimizing DUS testing for Chimonanthus praecox using feature selection based on a genetic algorithm. FRONTIERS IN PLANT SCIENCE 2024; 14:1328603. [PMID: 38312354 PMCID: PMC10835806 DOI: 10.3389/fpls.2023.1328603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 12/27/2023] [Indexed: 02/06/2024]
Abstract
Chimonanthus praecox is a famous traditional flower in China with high ornamental value. It has numerous varieties, yet its classification is highly disorganized. The distinctness, uniformity, and stability (DUS) test enables the classification and nomenclature of various species; thus, it can be used to classify the Chimonanthus varieties. In this study, flower traits were quantified using an automatic system based on pattern recognition instead of traditional manual measurement to improve the efficiency of DUS testing. A total of 42 features were quantified, including 28 features in the DUS guidelines and 14 new features proposed in this study. Eight algorithms were used to classify wintersweet, and the random forest (RF) algorithm performed the best when all features were used. The classification accuracy of the outer perianth was the highest when the features of the different parts were used for classification. A genetic algorithm was used as the feature selection algorithm to select a set of 22 reduced core features and improve the accuracy and efficiency of the classification. Using the core feature set, the classification accuracy of the RF model improved to 99.13%. Finally, K-means was used to construct a pedigree cluster tree of 23 varieties of wintersweet; evidently, wintersweet was clustered into a single class, which can be the basis for further study of genetic relationships among varieties. This study provides a novel method for DUS detection, variety identification, and pedigree analysis.
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Affiliation(s)
- Ting Zhu
- Chongqing Engineering Research Center for Floriculture, Key Laboratory of Agricultural Biosafety and Green Production of Upper Yangtze River (Ministry of Education), College of Horticulture and Landscape Architecture, Southwest University, Chongqing, China
| | - Yaoyao Feng
- College of Computer and Information Science, Southwest University, Chongqing, China
| | - Xiaoxuan Dong
- College of Computer and Information Science, Southwest University, Chongqing, China
| | - Ximeng Yang
- Chongqing Engineering Research Center for Floriculture, Key Laboratory of Agricultural Biosafety and Green Production of Upper Yangtze River (Ministry of Education), College of Horticulture and Landscape Architecture, Southwest University, Chongqing, China
| | - Bin Liu
- Chongqing Engineering Research Center for Floriculture, Key Laboratory of Agricultural Biosafety and Green Production of Upper Yangtze River (Ministry of Education), College of Horticulture and Landscape Architecture, Southwest University, Chongqing, China
| | - Puying Yuan
- Garden and Flower Research Center, Horticultural Research Institute of Sichuan Academy of Agricultural Science, Chengdu, China
| | - Xingrong Song
- Garden and Flower Research Center, Horticultural Research Institute of Sichuan Academy of Agricultural Science, Chengdu, China
| | - Shanxiong Chen
- College of Computer and Information Science, Southwest University, Chongqing, China
| | - Shunzhao Sui
- Chongqing Engineering Research Center for Floriculture, Key Laboratory of Agricultural Biosafety and Green Production of Upper Yangtze River (Ministry of Education), College of Horticulture and Landscape Architecture, Southwest University, Chongqing, China
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49
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Hui TX, Kasim S, Aziz IA, Fudzee MFM, Haron NS, Sutikno T, Hassan R, Mahdin H, Sen SC. Robustness evaluations of pathway activity inference methods on gene expression data. BMC Bioinformatics 2024; 25:23. [PMID: 38216898 PMCID: PMC10785356 DOI: 10.1186/s12859-024-05632-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Accepted: 01/02/2024] [Indexed: 01/14/2024] Open
Abstract
BACKGROUND With the exponential growth of high-throughput technologies, multiple pathway analysis methods have been proposed to estimate pathway activities from gene expression profiles. These pathway activity inference methods can be divided into two main categories: non-Topology-Based (non-TB) and Pathway Topology-Based (PTB) methods. Although some review and survey articles discussed the topic from different aspects, there is a lack of systematic assessment and comparisons on the robustness of these approaches. RESULTS Thus, this study presents comprehensive robustness evaluations of seven widely used pathway activity inference methods using six cancer datasets based on two assessments. The first assessment seeks to investigate the robustness of pathway activity in pathway activity inference methods, while the second assessment aims to assess the robustness of risk-active pathways and genes predicted by these methods. The mean reproducibility power and total number of identified informative pathways and genes were evaluated. Based on the first assessment, the mean reproducibility power of pathway activity inference methods generally decreased as the number of pathway selections increased. Entropy-based Directed Random Walk (e-DRW) distinctly outperformed other methods in exhibiting the greatest reproducibility power across all cancer datasets. On the other hand, the second assessment shows that no methods provide satisfactory results across datasets. CONCLUSION However, PTB methods generally appear to perform better in producing greater reproducibility power and identifying potential cancer markers compared to non-TB methods.
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Affiliation(s)
- Tay Xin Hui
- Soft Computing and Data Mining Center, Faculty of Computer Sciences and Information Technology, Universiti Tun Hussein Onn Malaysia (UTHM), 83000, Batu Pahat, Malaysia
| | - Shahreen Kasim
- Soft Computing and Data Mining Center, Faculty of Computer Sciences and Information Technology, Universiti Tun Hussein Onn Malaysia (UTHM), 83000, Batu Pahat, Malaysia.
| | - Izzatdin Abdul Aziz
- Computer and Information Sciences Department (CISD), Universiti Teknologi PETRONAS (UTP), 32610, Seri Iskandar, Malaysia
| | - Mohd Farhan Md Fudzee
- Soft Computing and Data Mining Center, Faculty of Computer Sciences and Information Technology, Universiti Tun Hussein Onn Malaysia (UTHM), 83000, Batu Pahat, Malaysia
| | - Nazleeni Samiha Haron
- Computer and Information Sciences Department (CISD), Universiti Teknologi PETRONAS (UTP), 32610, Seri Iskandar, Malaysia
| | - Tole Sutikno
- Department of Electrical Engineering, Universitas Ahmad Dahlan (UAD), 55166, Yogyakarta, Indonesia
| | - Rohayanti Hassan
- Faculty of Electrical Engineering, Universiti Teknologi Malaysia (UTM), 81310, Johor Bahru, Malaysia
| | - Hairulnizam Mahdin
- Soft Computing and Data Mining Center, Faculty of Computer Sciences and Information Technology, Universiti Tun Hussein Onn Malaysia (UTHM), 83000, Batu Pahat, Malaysia
| | - Seah Choon Sen
- Faculty of Computing, Universiti Teknologi Malaysia (UTM), 81310, Johor Bahru, Malaysia
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50
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Zou J, Shah O, Chiu YC, Ma T, Atkinson JM, Oesterreich S, Lee AV, Tseng GC. Systems approach for congruence and selection of cancer models towards precision medicine. PLoS Comput Biol 2024; 20:e1011754. [PMID: 38198519 PMCID: PMC10805322 DOI: 10.1371/journal.pcbi.1011754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 01/23/2024] [Accepted: 12/12/2023] [Indexed: 01/12/2024] Open
Abstract
Cancer models are instrumental as a substitute for human studies and to expedite basic, translational, and clinical cancer research. For a given cancer type, a wide selection of models, such as cell lines, patient-derived xenografts, organoids and genetically modified murine models, are often available to researchers. However, how to quantify their congruence to human tumors and to select the most appropriate cancer model is a largely unsolved issue. Here, we present Congruence Analysis and Selection of CAncer Models (CASCAM), a statistical and machine learning framework for authenticating and selecting the most representative cancer models in a pathway-specific manner using transcriptomic data. CASCAM provides harmonization between human tumor and cancer model omics data, systematic congruence quantification, and pathway-based topological visualization to determine the most appropriate cancer model selection. The systems approach is presented using invasive lobular breast carcinoma (ILC) subtype and suggesting CAMA1 followed by UACC3133 as the most representative cell lines for ILC research. Two additional case studies for triple negative breast cancer (TNBC) and patient-derived xenograft/organoid (PDX/PDO) are further investigated. CASCAM is generalizable to any cancer subtype and will authenticate cancer models for faithful non-human preclinical research towards precision medicine.
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Affiliation(s)
- Jian Zou
- Department of Statistics, School of Public Health, Chongqing Medical University, Chongqing, China
| | - Osama Shah
- Women’s Cancer Research Center, UPMC Hillman Cancer Center (HCC), Pittsburgh, Pennsylvania, United States of America
- Magee-Womens Research Institute, Pittsburgh, Pennsylvania, United States of America
- Department of Pharmacology & Chemical Biology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Yu-Chiao Chiu
- Cancer Therapeutics Program, UPMC Hillman Cancer Center (HCC), Pittsburgh, Pennsylvania, United States of America
- Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Tianzhou Ma
- Department of Epidemiology and Biostatistics, University of Maryland, College Park, Maryland, United States of America
| | - Jennifer M. Atkinson
- Women’s Cancer Research Center, UPMC Hillman Cancer Center (HCC), Pittsburgh, Pennsylvania, United States of America
- Magee-Womens Research Institute, Pittsburgh, Pennsylvania, United States of America
- Department of Pharmacology & Chemical Biology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Steffi Oesterreich
- Women’s Cancer Research Center, UPMC Hillman Cancer Center (HCC), Pittsburgh, Pennsylvania, United States of America
- Magee-Womens Research Institute, Pittsburgh, Pennsylvania, United States of America
- Department of Pharmacology & Chemical Biology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Adrian V. Lee
- Women’s Cancer Research Center, UPMC Hillman Cancer Center (HCC), Pittsburgh, Pennsylvania, United States of America
- Magee-Womens Research Institute, Pittsburgh, Pennsylvania, United States of America
- Department of Pharmacology & Chemical Biology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - George C. Tseng
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
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