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Karaglani M, Agorastos A, Panagopoulou M, Parlapani E, Athanasis P, Bitsios P, Tzitzikou K, Theodosiou T, Iliopoulos I, Bozikas VP, Chatzaki E. A novel blood-based epigenetic biosignature in first-episode schizophrenia patients through automated machine learning. Transl Psychiatry 2024; 14:257. [PMID: 38886359 PMCID: PMC11183091 DOI: 10.1038/s41398-024-02946-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Revised: 05/15/2024] [Accepted: 05/17/2024] [Indexed: 06/20/2024] Open
Abstract
Schizophrenia (SCZ) is a chronic, severe, and complex psychiatric disorder that affects all aspects of personal functioning. While SCZ has a very strong biological component, there are still no objective diagnostic tests. Lately, special attention has been given to epigenetic biomarkers in SCZ. In this study, we introduce a three-step, automated machine learning (AutoML)-based, data-driven, biomarker discovery pipeline approach, using genome-wide DNA methylation datasets and laboratory validation, to deliver a highly performing, blood-based epigenetic biosignature of diagnostic clinical value in SCZ. Publicly available blood methylomes from SCZ patients and healthy individuals were analyzed via AutoML, to identify SCZ-specific biomarkers. The methylation of the identified genes was then analyzed by targeted qMSP assays in blood gDNA of 30 first-episode drug-naïve SCZ patients and 30 healthy controls (CTRL). Finally, AutoML was used to produce an optimized disease-specific biosignature based on patient methylation data combined with demographics. AutoML identified a SCZ-specific set of novel gene methylation biomarkers including IGF2BP1, CENPI, and PSME4. Functional analysis investigated correlations with SCZ pathology. Methylation levels of IGF2BP1 and PSME4, but not CENPI were found to differ, IGF2BP1 being higher and PSME4 lower in the SCZ group as compared to the CTRL group. Additional AutoML classification analysis of our experimental patient data led to a five-feature biosignature including all three genes, as well as age and sex, that discriminated SCZ patients from healthy individuals [AUC 0.755 (0.636, 0.862) and average precision 0.758 (0.690, 0.825)]. In conclusion, this three-step pipeline enabled the discovery of three novel genes and an epigenetic biosignature bearing potential value as promising SCZ blood-based diagnostics.
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Affiliation(s)
- Makrina Karaglani
- Laboratory of Pharmacology, Department of Medicine, Democritus University of Thrace, GR-68132, Alexandroupolis, Greece
- Institute of Agri-food and Life Sciences, University Research & Innovation Center, H.M.U.R.I.C., Hellenic Mediterranean University, GR-71003, Crete, Greece
| | - Agorastos Agorastos
- Institute of Agri-food and Life Sciences, University Research & Innovation Center, H.M.U.R.I.C., Hellenic Mediterranean University, GR-71003, Crete, Greece
- II. Department of Psychiatry, Faculty of Health Sciences, School of Medicine, Aristotle University of Thessaloniki, GR-56430, Thessaloniki, Greece
| | - Maria Panagopoulou
- Laboratory of Pharmacology, Department of Medicine, Democritus University of Thrace, GR-68132, Alexandroupolis, Greece
- Institute of Agri-food and Life Sciences, University Research & Innovation Center, H.M.U.R.I.C., Hellenic Mediterranean University, GR-71003, Crete, Greece
| | - Eleni Parlapani
- Ι. Department of Psychiatry, Faculty of Health Sciences, School of Medicine, Aristotle University of Thessaloniki, GR-56429, Thessaloniki, Greece
| | - Panagiotis Athanasis
- II. Department of Psychiatry, Faculty of Health Sciences, School of Medicine, Aristotle University of Thessaloniki, GR-56430, Thessaloniki, Greece
| | - Panagiotis Bitsios
- Department of Psychiatry and Behavioral Sciences, Faculty of Medicine, University of Crete, GR-71500, Heraklion, Greece
| | - Konstantina Tzitzikou
- Laboratory of Pharmacology, Department of Medicine, Democritus University of Thrace, GR-68132, Alexandroupolis, Greece
| | - Theodosis Theodosiou
- Laboratory of Pharmacology, Department of Medicine, Democritus University of Thrace, GR-68132, Alexandroupolis, Greece
- ABCureD P.C, GR-68131, Alexandroupolis, Greece
| | - Ioannis Iliopoulos
- Division of Basic Sciences, School of Medicine, University of Crete, GR-71003, Heraklion, Greece
| | - Vasilios-Panteleimon Bozikas
- II. Department of Psychiatry, Faculty of Health Sciences, School of Medicine, Aristotle University of Thessaloniki, GR-56430, Thessaloniki, Greece
| | - Ekaterini Chatzaki
- Laboratory of Pharmacology, Department of Medicine, Democritus University of Thrace, GR-68132, Alexandroupolis, Greece.
- Institute of Agri-food and Life Sciences, University Research & Innovation Center, H.M.U.R.I.C., Hellenic Mediterranean University, GR-71003, Crete, Greece.
- ABCureD P.C, GR-68131, Alexandroupolis, Greece.
- Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology, 70013, Heraklion, Greece.
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Krix S, Wilczynski E, Falgàs N, Sánchez-Valle R, Yoles E, Nevo U, Baruch K, Fröhlich H. Towards early diagnosis of Alzheimer's disease: advances in immune-related blood biomarkers and computational approaches. Front Immunol 2024; 15:1343900. [PMID: 38720902 PMCID: PMC11078023 DOI: 10.3389/fimmu.2024.1343900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Accepted: 04/08/2024] [Indexed: 05/12/2024] Open
Abstract
Alzheimer's disease has an increasing prevalence in the population world-wide, yet current diagnostic methods based on recommended biomarkers are only available in specialized clinics. Due to these circumstances, Alzheimer's disease is usually diagnosed late, which contrasts with the currently available treatment options that are only effective for patients at an early stage. Blood-based biomarkers could fill in the gap of easily accessible and low-cost methods for early diagnosis of the disease. In particular, immune-based blood-biomarkers might be a promising option, given the recently discovered cross-talk of immune cells of the central nervous system with those in the peripheral immune system. Here, we give a background on recent advances in research on brain-immune system cross-talk in Alzheimer's disease and review machine learning approaches, which can combine multiple biomarkers with further information (e.g. age, sex, APOE genotype) into predictive models supporting an earlier diagnosis. In addition, mechanistic modeling approaches, such as agent-based modeling open the possibility to model and analyze cell dynamics over time. This review aims to provide an overview of the current state of immune-system related blood-based biomarkers and their potential for the early diagnosis of Alzheimer's disease.
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Affiliation(s)
- Sophia Krix
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology (b-it), University of Bonn, Bonn, Germany
| | - Ella Wilczynski
- Department of Biomedical Engineering, The Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel
| | - Neus Falgàs
- Alzheimer’s Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, Fundació de Recerca Clínic Barcelona-Institut d'Investigacions Biomèdiques August Pi i Sunyer (FCRB-IDIBAPS), University of Barcelona, Barcelona, Spain
| | - Raquel Sánchez-Valle
- Alzheimer’s Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, Fundació de Recerca Clínic Barcelona-Institut d'Investigacions Biomèdiques August Pi i Sunyer (FCRB-IDIBAPS), University of Barcelona, Barcelona, Spain
| | - Eti Yoles
- ImmunoBrain Checkpoint Ltd., Rechovot, Israel
| | - Uri Nevo
- Department of Biomedical Engineering, The Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Kuti Baruch
- ImmunoBrain Checkpoint Ltd., Rechovot, Israel
| | - Holger Fröhlich
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology (b-it), University of Bonn, Bonn, Germany
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Ahmadi M, Javaheri D, Khajavi M, Danesh K, Hur J. A deeply supervised adaptable neural network for diagnosis and classification of Alzheimer's severity using multitask feature extraction. PLoS One 2024; 19:e0297996. [PMID: 38530836 DOI: 10.1371/journal.pone.0297996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Accepted: 01/16/2024] [Indexed: 03/28/2024] Open
Abstract
Alzheimer's disease is the most prevalent form of dementia, which is a gradual condition that begins with mild memory loss and progresses to difficulties communicating and responding to the environment. Recent advancements in neuroimaging techniques have resulted in large-scale multimodal neuroimaging data, leading to an increased interest in using deep learning for the early diagnosis and automated classification of Alzheimer's disease. This study uses machine learning (ML) methods to determine the severity level of Alzheimer's disease using MRI images, where the dataset consists of four levels of severity. A hybrid of 12 feature extraction methods is used to diagnose Alzheimer's disease severity, and six traditional machine learning methods are applied, including decision tree, K-nearest neighbor, linear discrimination analysis, Naïve Bayes, support vector machine, and ensemble learning methods. During training, optimization is performed to obtain the best solution for each classifier. Additionally, a CNN model is trained using a machine learning system algorithm to identify specific patterns. The accuracy of the Naïve Bayes, Support Vector Machines, K-nearest neighbor, Linear discrimination classifier, Decision tree, Ensembled learning, and presented CNN architecture are 67.5%, 72.3%, 74.5%, 65.6%, 62.4%, 73.8% and, 95.3%, respectively. Based on the results, the presented CNN approach outperforms other traditional machine learning methods to find Alzheimer severity.
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Affiliation(s)
- Mohsen Ahmadi
- Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, United States of America
| | - Danial Javaheri
- Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea
| | - Matin Khajavi
- Foster School of Businesses, University of Washington, Seattle, Washington, United States of America
| | - Kasra Danesh
- Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, United States of America
| | - Junbeom Hur
- Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea
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Javeed A, Anderberg P, Ghazi AN, Noor A, Elmståhl S, Berglund JS. Breaking barriers: a statistical and machine learning-based hybrid system for predicting dementia. Front Bioeng Biotechnol 2024; 11:1336255. [PMID: 38260734 PMCID: PMC10801181 DOI: 10.3389/fbioe.2023.1336255] [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: 11/10/2023] [Accepted: 12/05/2023] [Indexed: 01/24/2024] Open
Abstract
Introduction: Dementia is a condition (a collection of related signs and symptoms) that causes a continuing deterioration in cognitive function, and millions of people are impacted by dementia every year as the world population continues to rise. Conventional approaches for determining dementia rely primarily on clinical examinations, analyzing medical records, and administering cognitive and neuropsychological testing. However, these methods are time-consuming and costly in terms of treatment. Therefore, this study aims to present a noninvasive method for the early prediction of dementia so that preventive steps should be taken to avoid dementia. Methods: We developed a hybrid diagnostic system based on statistical and machine learning (ML) methods that used patient electronic health records to predict dementia. The dataset used for this study was obtained from the Swedish National Study on Aging and Care (SNAC), with a sample size of 43040 and 75 features. The newly constructed diagnostic extracts a subset of useful features from the dataset through a statistical method (F-score). For the classification, we developed an ensemble voting classifier based on five different ML models: decision tree (DT), naive Bayes (NB), logistic regression (LR), support vector machines (SVM), and random forest (RF). To address the problem of ML model overfitting, we used a cross-validation approach to evaluate the performance of the proposed diagnostic system. Various assessment measures, such as accuracy, sensitivity, specificity, receiver operating characteristic (ROC) curve, and Matthew's correlation coefficient (MCC), were used to thoroughly validate the devised diagnostic system's efficiency. Results: According to the experimental results, the proposed diagnostic method achieved the best accuracy of 98.25%, as well as sensitivity of 97.44%, specificity of 95.744%, and MCC of 0.7535. Discussion: The effectiveness of the proposed diagnostic approach is compared to various cutting-edge feature selection techniques and baseline ML models. From experimental results, it is evident that the proposed diagnostic system outperformed the prior feature selection strategies and baseline ML models regarding accuracy.
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Affiliation(s)
- Ashir Javeed
- Department of Health, Blekinge Institute of Technology, Karlskrona, Sweden
| | - Peter Anderberg
- Department of Health, Blekinge Institute of Technology, Karlskrona, Sweden
- School of Health Sciences, University of Skövde, Skövde, Sweden
| | - Ahmad Nauman Ghazi
- Department of Software Engineering, Blekinge Institute of Technology, Karlskrona, Sweden
| | - Adeeb Noor
- Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Sölve Elmståhl
- EpiHealth: Epidemiology for Health, Lund University, SUS Malmö, Malmö, Sweden
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Lin E, Lin CH, Lane HY. Inference of social cognition in schizophrenia patients with neurocognitive domains and neurocognitive tests using automated machine learning. Asian J Psychiatr 2024; 91:103866. [PMID: 38128351 DOI: 10.1016/j.ajp.2023.103866] [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: 08/11/2023] [Revised: 12/07/2023] [Accepted: 12/09/2023] [Indexed: 12/23/2023]
Abstract
AIM It has been suggested that single neurocognitive domain or neurocognitive test can be used to determine the overall cognitive function in schizophrenia using machine learning algorithms. It is unknown whether social cognition in schizophrenia patients can be estimated with machine learning based on neurocognitive domains or neurocognitive tests. METHODS To predict social cognition in schizophrenia, we applied an automated machine learning (AutoML) framework resulting from the analysis of predictive factors such as six neurocognitive domain scores and nine neurocognitive test scores of 380 schizophrenia patients in the Taiwanese population. Four clinical parameters (i.e., age, gender, subgroup, and education) were also used as predictive factors. We utilized an AutoML framework called Tree-based Pipeline Optimization Tool (TPOT) to generate predictive pipelines automatically. RESULTS The analysis revealed that all neurocognitive domains and tests except the reasoning and problem solving domain/test showed significant associations with social cognition. In addition, a TPOT-generated pipeline can best predict social cognition in schizophrenia using seven predictive factors, including five neurocognitive domains (i.e., speed of processing, sustained attention, working memory, verbal learning and memory, and visual learning and memory) and two clinical parameters (i.e., age and gender). This predictive pipeline consists of machine learning algorithms such as function transformers, an approximate feature map, independent component analysis, and linear regression. CONCLUSION The study indicates that an AutoML framework such as TPOT may provide a promising way to produce truly effective machine learning pipelines for predicting social cognition in schizophrenia using neurocognitive domains and/or neurocognitive tests.
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Affiliation(s)
- Eugene Lin
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA; Department of Electrical & Computer Engineering, University of Washington, Seattle, WA 98195, USA; Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan
| | - Chieh-Hsin Lin
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan; Department of Psychiatry, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan; School of Medicine, Chang Gung University, Taoyuan, Taiwan.
| | - Hsien-Yuan Lane
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan; Department of Psychiatry, China Medical University Hospital, Taichung, Taiwan; Brain Disease Research Center, China Medical University Hospital, Taichung, Taiwan; Department of Psychology, College of Medical and Health Sciences, Asia University, Taichung, Taiwan.
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6
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Thomaidis GV, Papadimitriou K, Michos S, Chartampilas E, Tsamardinos I. A characteristic cerebellar biosignature for bipolar disorder, identified with fully automatic machine learning. IBRO Neurosci Rep 2023; 15:77-89. [PMID: 38025660 PMCID: PMC10668096 DOI: 10.1016/j.ibneur.2023.06.008] [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: 01/06/2023] [Revised: 05/19/2023] [Accepted: 06/29/2023] [Indexed: 12/01/2023] Open
Abstract
Background Transcriptomic profile differences between patients with bipolar disorder and healthy controls can be identified using machine learning and can provide information about the potential role of the cerebellum in the pathogenesis of bipolar disorder.With this aim, user-friendly, fully automated machine learning algorithms can achieve extremely high classification scores and disease-related predictive biosignature identification, in short time frames and scaled down to small datasets. Method A fully automated machine learning platform, based on the most suitable algorithm selection and relevant set of hyper-parameter values, was applied on a preprocessed transcriptomics dataset, in order to produce a model for biosignature selection and to classify subjects into groups of patients and controls. The parent GEO datasets were originally produced from the cerebellar and parietal lobe tissue of deceased bipolar patients and healthy controls, using Affymetrix Human Gene 1.0 ST Array. Results Patients and controls were classified into two separate groups, with no close-to-the-boundary cases, and this classification was based on the cerebellar transcriptomic biosignature of 25 features (genes), with Area Under Curve 0.929 and Average Precision 0.955. The biosignature includes both genes connected before to bipolar disorder, depression, psychosis or epilepsy, as well as genes not linked before with any psychiatric disease. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed participation of 4 identified features in 6 pathways which have also been associated with bipolar disorder. Conclusion Automated machine learning (AutoML) managed to identify accurately 25 genes that can jointly - in a multivariate-fashion - separate bipolar patients from healthy controls with high predictive power. The discovered features lead to new biological insights. Machine Learning (ML) analysis considers the features in combination (in contrast to standard differential expression analysis), removing both irrelevant as well as redundant markers, and thus, focusing to biological interpretation.
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Affiliation(s)
- Georgios V. Thomaidis
- Greek National Health System, Psychiatric Department, Katerini General Hospital, Katerini, Greece
| | - Konstantinos Papadimitriou
- Greek National Health System, G. Papanikolaou General Hospital, Organizational Unit - Psychiatric Hospital of Thessaloniki, Thessaloniki, Greece
| | | | - Evangelos Chartampilas
- Laboratory of Radiology, AHEPA General Hospital, University of Thessaloniki, Thessaloniki, Greece
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Kelly J, Moyeed R, Carroll C, Luo S, Li X. Blood biomarker-based classification study for neurodegenerative diseases. Sci Rep 2023; 13:17191. [PMID: 37821485 PMCID: PMC10567903 DOI: 10.1038/s41598-023-43956-4] [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: 02/23/2023] [Accepted: 09/30/2023] [Indexed: 10/13/2023] Open
Abstract
As the population ages, neurodegenerative diseases are becoming more prevalent, making it crucial to comprehend the underlying disease mechanisms and identify biomarkers to allow for early diagnosis and effective screening for clinical trials. Thanks to advancements in gene expression profiling, it is now possible to search for disease biomarkers on an unprecedented scale.Here we applied a selection of five machine learning (ML) approaches to identify blood-based biomarkers for Alzheimer's (AD) and Parkinson's disease (PD) with the application of multiple feature selection methods. Based on ROC AUC performance, one optimal random forest (RF) model was discovered for AD with 159 gene markers (ROC-AUC = 0.886), while one optimal RF model was discovered for PD (ROC-AUC = 0.743). Additionally, in comparison to traditional ML approaches, deep learning approaches were applied to evaluate their potential applications in future works. We demonstrated that convolutional neural networks perform consistently well across both the Alzheimer's (ROC AUC = 0.810) and Parkinson's (ROC AUC = 0.715) datasets, suggesting its potential in gene expression biomarker detection with increased tuning of their architecture.
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Affiliation(s)
- Jack Kelly
- Faculty of Medicine, Biology and Health, Centre for Biostatistics, School of Health Sciences, University of Manchester, Manchester, UK.
- Faculty of Health, University of Plymouth, Plymouth, PL6 8BU, UK.
| | - Rana Moyeed
- Faculty of Science and Engineering, University of Plymouth, Plymouth, PL6 8BU, UK
| | - Camille Carroll
- Faculty of Health, University of Plymouth, Plymouth, PL6 8BU, UK
| | - Shouqing Luo
- Faculty of Health, University of Plymouth, Plymouth, PL6 8BU, UK
| | - Xinzhong Li
- School of Health and Life Sciences, Teesside University, Middlesbrough, TS1 3BX, UK.
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Musigmann M, Nacul NG, Kasap DN, Heindel W, Mannil M. Use Test of Automated Machine Learning in Cancer Diagnostics. Diagnostics (Basel) 2023; 13:2315. [PMID: 37510059 PMCID: PMC10378334 DOI: 10.3390/diagnostics13142315] [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: 05/31/2023] [Revised: 06/30/2023] [Accepted: 07/03/2023] [Indexed: 07/30/2023] Open
Abstract
Our aim is to investigate the added value of automated machine learning (AutoML) for potential future applications in cancer diagnostics. Using two important diagnostic questions, the non-invasive determination of IDH mutation status and ATRX status, we analyze whether it is possible to use AutoML to develop models that are comparable in performance to conventional machine learning models (ML) developed by experts. For this purpose, we develop AutoML models using different feature preselection methods and compare the results with previously developed conventional ML models. The cohort used for our study comprises T2-weighted MRI images of 124 patients with histologically confirmed gliomas. Using AutoML, we were able to develop sophisticated models in a very short time with only a few lines of computer code. In predicting IDH mutation status, we obtained a mean AUC of 0.7400 and a mean AUPRC of 0.8582. ATRX mutation status was predicted with very similar discriminatory power, with a mean AUC of 0.7810 and a mean AUPRC of 0.8511. In both cases, AutoML was even able to achieve a discriminatory power slightly above that of the respective conventionally developed models in a very short computing time, thus making such methods accessible to non-experts in the near future.
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Affiliation(s)
- Manfred Musigmann
- University Clinic for Radiology, University Hospital Muenster, WWU Muenster, Albert-Schweitzer-Campus 1, DE-48149 Muenster, Germany
| | - Nabila Gala Nacul
- University Clinic for Radiology, University Hospital Muenster, WWU Muenster, Albert-Schweitzer-Campus 1, DE-48149 Muenster, Germany
| | - Dilek N Kasap
- University Clinic for Radiology, University Hospital Muenster, WWU Muenster, Albert-Schweitzer-Campus 1, DE-48149 Muenster, Germany
| | - Walter Heindel
- University Clinic for Radiology, University Hospital Muenster, WWU Muenster, Albert-Schweitzer-Campus 1, DE-48149 Muenster, Germany
| | - Manoj Mannil
- University Clinic for Radiology, University Hospital Muenster, WWU Muenster, Albert-Schweitzer-Campus 1, DE-48149 Muenster, Germany
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Lee J, Westphal M, Vali Y, Boursier J, Petta S, Ostroff R, Alexander L, Chen Y, Fournier C, Geier A, Francque S, Wonders K, Tiniakos D, Bedossa P, Allison M, Papatheodoridis G, Cortez-Pinto H, Pais R, Dufour JF, Leeming DJ, Harrison S, Cobbold J, Holleboom AG, Yki-Järvinen H, Crespo J, Ekstedt M, Aithal GP, Bugianesi E, Romero-Gomez M, Torstenson R, Karsdal M, Yunis C, Schattenberg JM, Schuppan D, Ratziu V, Brass C, Duffin K, Zwinderman K, Pavlides M, Anstee QM, Bossuyt PM. Machine learning algorithm improves the detection of NASH (NAS-based) and at-risk NASH: A development and validation study. Hepatology 2023; 78:258-271. [PMID: 36994719 DOI: 10.1097/hep.0000000000000364] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 12/22/2022] [Indexed: 03/31/2023]
Abstract
BACKGROUND AND AIMS Detecting NASH remains challenging, while at-risk NASH (steatohepatitis and F≥ 2) tends to progress and is of interest for drug development and clinical application. We developed prediction models by supervised machine learning techniques, with clinical data and biomarkers to stage and grade patients with NAFLD. APPROACH AND RESULTS Learning data were collected in the Liver Investigation: Testing Marker Utility in Steatohepatitis metacohort (966 biopsy-proven NAFLD adults), staged and graded according to NASH CRN. Conditions of interest were the clinical trial definition of NASH (NAS ≥ 4;53%), at-risk NASH (NASH with F ≥ 2;35%), significant (F ≥ 2;47%), and advanced fibrosis (F ≥ 3;28%). Thirty-five predictors were included. Missing data were handled by multiple imputations. Data were randomly split into training/validation (75/25) sets. A gradient boosting machine was applied to develop 2 models for each condition: clinical versus extended (clinical and biomarkers). Two variants of the NASH and at-risk NASH models were constructed: direct and composite models.Clinical gradient boosting machine models for steatosis/inflammation/ballooning had AUCs of 0.94/0.79/0.72. There were no improvements when biomarkers were included. The direct NASH model produced AUCs (clinical/extended) of 0.61/0.65. The composite NASH model performed significantly better (0.71) for both variants. The composite at-risk NASH model had an AUC of 0.83 (clinical and extended), an improvement over the direct model. Significant fibrosis models had AUCs (clinical/extended) of 0.76/0.78. The extended advanced fibrosis model (0.86) performed significantly better than the clinical version (0.82). CONCLUSIONS Detection of NASH and at-risk NASH can be improved by constructing independent machine learning models for each component, using only clinical predictors. Adding biomarkers only improved the accuracy of fibrosis.
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Affiliation(s)
- Jenny Lee
- Department of Epidemiology and Data Science, Amsterdam UMC, Amsterdam, the Netherlands
| | - Max Westphal
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
| | - Yasaman Vali
- Department of Epidemiology and Data Science, Amsterdam UMC, Amsterdam, the Netherlands
| | - Jerome Boursier
- Department of Hepatology, Angers University Hospital, Angers, France
| | - Salvatorre Petta
- Section of Gastroenterology and Hepatology, Promozione della Salute, Materno-Infantile, di Medicina Interna e Specialistica di Eccellenza, Department, University of Palermo, Palermo, Italy
| | | | | | - Yu Chen
- Lilly Research Laboratories, Eli Lilly and Company Ltd (LLY), Indianapolis, Indiana, USA
| | | | - Andreas Geier
- Division of Hepatology, Department of Medicine II, Wurzburg University Hospital, Wurzburg, Germany
| | - Sven Francque
- Department of Gastroenterology Hepatology, Antwerp University Hospital, and Laboratory of Experimental Medicine and Paediatrics, University of Antwerp, Belgium
| | - Kristy Wonders
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Dina Tiniakos
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
- Department of Pathology, Aretaieion Hospital, national and Kapodistrian University of Athens, Athens, Greece
| | - Pierre Bedossa
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Mike Allison
- Liver Unit, Department of Medicine, Cambridge NIHR Biomedical Research Centre, Cambridge University NHS Foundation Trust, CB2 0QQ, Cambridge, UK
| | - Georgios Papatheodoridis
- Gastroenterology Department, National and Kapodistrian University of Athens, General Hospital of Athens "Laiko", Athens, Greece
| | - Helena Cortez-Pinto
- Clínica Universitária de Gastrenterologia, Faculdade de Medicina, Universidade de Lisboa, Portugal
| | - Raluca Pais
- Assistance Publique-Hôpitaux de Paris, hôpital Pitié Salpêtrière, Sorbonne University, ICAN (Institute of Cardiometabolism and Nutrition), Paris, France
| | - Jean-Francois Dufour
- Hepatology, Department of Biomedical Research, University of Bern, Bern, Switzerland
| | | | - Stephen Harrison
- Department of Gastroenterology and Hepatology, Oxford NIHR Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - Jeremy Cobbold
- Department of Gastroenterology and Hepatology, Oxford NIHR Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - Adriaan G Holleboom
- Department of Internal and Vascular Medicine, Amsterdam University Medical Centres, location AMC, Amsterdam, the Netherlands
| | - Hannele Yki-Järvinen
- Department of Medicine, University of Helsinki and Helsinki University Hospital, Finland; Minerva Foundation Institute for Medical Research, Helsinki, Finland
| | - Javier Crespo
- Department of Gastroenterology and Hepatology, University Hospital Marques de Valdecilla. Research Institute Valdecilla-IDIVAL, Santander, Spain
| | - Mattias Ekstedt
- Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Guruprasad P Aithal
- Nottingham Digestive Diseases Centre, School of Medicine, NIHR Nottingham Biomedical Research Centre, Nottingham University Hospitals NHS Trust and The University of Nottingham, Nottingham, UK
| | - Elisabetta Bugianesi
- Department of Medical Sciences, Division of Gastro-Hepatology, A.O. Città della Salute e della Scienza di Torino, University of Turin, Turin, Italy
| | - Manuel Romero-Gomez
- UCM Digestive Diseases, ciberehd, Virgen del Rocio University Hospital. Institute of Biomedicine of Seville (CSIC/HUVR/US), Department of Medicine, University of Seville, Seville, Spain
| | - Richard Torstenson
- Cardiovascular, Renal and Metabolism Regulatory Affairs, AstraZeneca, Mölndal, Sweden
| | | | - Carla Yunis
- Internal Medicine and Hospital, Global Product Development, Pfizer, Inc, New York, New York, USA
| | - Jörn M Schattenberg
- Metabolic Liver Research Program, I. Department of Medicine, University Medical Center Mainz, Mainz, Germany
| | - Detlef Schuppan
- Institute of Translational Immunology and Research Center for Immune Therapy, University Medical Center Mainz, Mainz, Germany
- Division of Gastroenterology, Beth Israel Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Vlad Ratziu
- Assistance Publique-Hôpitaux de Paris, hôpital Pitié Salpêtrière, Sorbonne University, ICAN (Institute of Cardiometabolism and Nutrition), Paris, France
| | - Clifford Brass
- Novartis Pharmaceuticals Corporation, East Hanover, New Jersey
| | - Kevin Duffin
- Lilly Research Laboratories, Eli Lilly and Company Ltd (LLY), Indianapolis, Indiana, USA
| | - Koos Zwinderman
- Department of Epidemiology and Data Science, Amsterdam UMC, Amsterdam, the Netherlands
| | | | - Quentin M Anstee
- Department of Gastroenterology Hepatology, Antwerp University Hospital, and Laboratory of Experimental Medicine and Paediatrics, University of Antwerp, Belgium
- Newcastle NIHR Biomedical Research Centre, Newcastle upon Tyne Hospitals NHS Trust, Newcastle upon Tyne, UK
| | - Patrick M Bossuyt
- Department of Epidemiology and Data Science, Amsterdam UMC, Amsterdam, the Netherlands
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10
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Hossain E, Rana R, Higgins N, Soar J, Barua PD, Pisani AR, Turner K. Natural Language Processing in Electronic Health Records in relation to healthcare decision-making: A systematic review. Comput Biol Med 2023; 155:106649. [PMID: 36805219 DOI: 10.1016/j.compbiomed.2023.106649] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 01/04/2023] [Accepted: 02/07/2023] [Indexed: 02/12/2023]
Abstract
BACKGROUND Natural Language Processing (NLP) is widely used to extract clinical insights from Electronic Health Records (EHRs). However, the lack of annotated data, automated tools, and other challenges hinder the full utilisation of NLP for EHRs. Various Machine Learning (ML), Deep Learning (DL) and NLP techniques are studied and compared to understand the limitations and opportunities in this space comprehensively. METHODOLOGY After screening 261 articles from 11 databases, we included 127 papers for full-text review covering seven categories of articles: (1) medical note classification, (2) clinical entity recognition, (3) text summarisation, (4) deep learning (DL) and transfer learning architecture, (5) information extraction, (6) Medical language translation and (7) other NLP applications. This study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. RESULT AND DISCUSSION EHR was the most commonly used data type among the selected articles, and the datasets were primarily unstructured. Various ML and DL methods were used, with prediction or classification being the most common application of ML or DL. The most common use cases were: the International Classification of Diseases, Ninth Revision (ICD-9) classification, clinical note analysis, and named entity recognition (NER) for clinical descriptions and research on psychiatric disorders. CONCLUSION We find that the adopted ML models were not adequately assessed. In addition, the data imbalance problem is quite important, yet we must find techniques to address this underlining problem. Future studies should address key limitations in studies, primarily identifying Lupus Nephritis, Suicide Attempts, perinatal self-harmed and ICD-9 classification.
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Affiliation(s)
- Elias Hossain
- School of Engineering & Physical Sciences, North South University, Dhaka 1229, Bangladesh.
| | - Rajib Rana
- School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield Central QLD 4300, Australia
| | - Niall Higgins
- School of Management and Enterprise, University of Southern Queensland, Darling Heights QLD 4350, Australia; School of Nursing, Queensland University of Technology, Kelvin Grove, Brisbane, QLD 4000, Australia; Metro North Mental Health, Herston QLD 4029, Australia
| | - Jeffrey Soar
- School of Business, University of Southern Queensland, Springfield Central QLD 4300, Australia
| | - Prabal Datta Barua
- School of Business, University of Southern Queensland, Springfield Central QLD 4300, Australia
| | - Anthony R Pisani
- Center for the Study and Prevention of Suicide, University of Rochester, Rochester, NY, United States
| | - Kathryn Turner
- School of Nursing, Queensland University of Technology, Kelvin Grove, Brisbane, QLD 4000, Australia
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11
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Early Prediction of Dementia Using Feature Extraction Battery (FEB) and Optimized Support Vector Machine (SVM) for Classification. Biomedicines 2023; 11:biomedicines11020439. [PMID: 36830975 PMCID: PMC9953011 DOI: 10.3390/biomedicines11020439] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Revised: 01/30/2023] [Accepted: 01/31/2023] [Indexed: 02/05/2023] Open
Abstract
Dementia is a cognitive disorder that mainly targets older adults. At present, dementia has no cure or prevention available. Scientists found that dementia symptoms might emerge as early as ten years before the onset of real disease. As a result, machine learning (ML) scientists developed various techniques for the early prediction of dementia using dementia symptoms. However, these methods have fundamental limitations, such as low accuracy and bias in machine learning (ML) models. To resolve the issue of bias in the proposed ML model, we deployed the adaptive synthetic sampling (ADASYN) technique, and to improve accuracy, we have proposed novel feature extraction techniques, namely, feature extraction battery (FEB) and optimized support vector machine (SVM) using radical basis function (rbf) for the classification of the disease. The hyperparameters of SVM are calibrated by employing the grid search approach. It is evident from the experimental results that the newly pr oposed model (FEB-SVM) improves the dementia prediction accuracy of the conventional SVM by 6%. The proposed model (FEB-SVM) obtained 98.28% accuracy on training data and a testing accuracy of 93.92%. Along with accuracy, the proposed model obtained a precision of 91.80%, recall of 86.59, F1-score of 89.12%, and Matthew's correlation coefficient (MCC) of 0.4987. Moreover, the newly proposed model (FEB-SVM) outperforms the 12 state-of-the-art ML models that the researchers have recently presented for dementia prediction.
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12
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Javeed A, Dallora AL, Berglund JS, Ali A, Ali L, Anderberg P. Machine Learning for Dementia Prediction: A Systematic Review and Future Research Directions. J Med Syst 2023; 47:17. [PMID: 36720727 PMCID: PMC9889464 DOI: 10.1007/s10916-023-01906-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Accepted: 01/03/2023] [Indexed: 02/02/2023]
Abstract
Nowadays, Artificial Intelligence (AI) and machine learning (ML) have successfully provided automated solutions to numerous real-world problems. Healthcare is one of the most important research areas for ML researchers, with the aim of developing automated disease prediction systems. One of the disease detection problems that AI and ML researchers have focused on is dementia detection using ML methods. Numerous automated diagnostic systems based on ML techniques for early prediction of dementia have been proposed in the literature. Few systematic literature reviews (SLR) have been conducted for dementia prediction based on ML techniques in the past. However, these SLR focused on a single type of data modality for the detection of dementia. Hence, the purpose of this study is to conduct a comprehensive evaluation of ML-based automated diagnostic systems considering different types of data modalities such as images, clinical-features, and voice data. We collected the research articles from 2011 to 2022 using the keywords dementia, machine learning, feature selection, data modalities, and automated diagnostic systems. The selected articles were critically analyzed and discussed. It was observed that image data driven ML models yields promising results in terms of dementia prediction compared to other data modalities, i.e., clinical feature-based data and voice data. Furthermore, this SLR highlighted the limitations of the previously proposed automated methods for dementia and presented future directions to overcome these limitations.
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Affiliation(s)
- Ashir Javeed
- Aging Research Center, Karolinska Institutet, Tomtebodavagen, Stockholm, 17165, Solna, Sweden
- Department of Health, Blekinge Institute of Technology, Valhallavägen 1, Karlskrona, 37141, Blekinge, Sweden
| | - Ana Luiza Dallora
- Department of Health, Blekinge Institute of Technology, Valhallavägen 1, Karlskrona, 37141, Blekinge, Sweden
| | - Johan Sanmartin Berglund
- Department of Health, Blekinge Institute of Technology, Valhallavägen 1, Karlskrona, 37141, Blekinge, Sweden.
| | - Arif Ali
- Department of Computer Science, University of Science and Technology Bannu, Township, Bannu, 28100, Khyber-Pakhtunkhwa, Pakistan
| | - Liaqata Ali
- Department of Electrical Engineering, University of Science and Technology Bannu, Township, Bannu, 28100, Khyber-Pakhtunkhwa, Pakistan
| | - Peter Anderberg
- Department of Health, Blekinge Institute of Technology, Valhallavägen 1, Karlskrona, 37141, Blekinge, Sweden
- School of Health Sciences, University of Skovde, Högskolevägen 1, Skövde, SE-541 28, Skövde, Sweden
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13
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Zhang Y, Ghose U, Buckley NJ, Engelborghs S, Sleegers K, Frisoni GB, Wallin A, Lleó A, Popp J, Martinez-Lage P, Legido-Quigley C, Barkhof F, Zetterberg H, Visser PJ, Bertram L, Lovestone S, Nevado-Holgado AJ, Shi L. Predicting AT(N) pathologies in Alzheimer's disease from blood-based proteomic data using neural networks. Front Aging Neurosci 2022; 14:1040001. [PMID: 36523958 PMCID: PMC9746615 DOI: 10.3389/fnagi.2022.1040001] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 11/04/2022] [Indexed: 05/19/2024] Open
Abstract
BACKGROUND AND OBJECTIVE Blood-based biomarkers represent a promising approach to help identify early Alzheimer's disease (AD). Previous research has applied traditional machine learning (ML) to analyze plasma omics data and search for potential biomarkers, but the most modern ML methods based on deep learning has however been scarcely explored. In the current study, we aim to harness the power of state-of-the-art deep learning neural networks (NNs) to identify plasma proteins that predict amyloid, tau, and neurodegeneration (AT[N]) pathologies in AD. METHODS We measured 3,635 proteins using SOMAscan in 881 participants from the European Medical Information Framework for AD Multimodal Biomarker Discovery study (EMIF-AD MBD). Participants underwent measurements of brain amyloid β (Aβ) burden, phosphorylated tau (p-tau) burden, and total tau (t-tau) burden to determine their AT(N) statuses. We ranked proteins by their association with Aβ, p-tau, t-tau, and AT(N), and fed the top 100 proteins along with age and apolipoprotein E (APOE) status into NN classifiers as input features to predict these four outcomes relevant to AD. We compared NN performance of using proteins, age, and APOE genotype with performance of using age and APOE status alone to identify protein panels that optimally improved the prediction over these main risk factors. Proteins that improved the prediction for each outcome were aggregated and nominated for pathway enrichment and protein-protein interaction enrichment analysis. RESULTS Age and APOE alone predicted Aβ, p-tau, t-tau, and AT(N) burden with area under the curve (AUC) scores of 0.748, 0.662, 0.710, and 0.795. The addition of proteins significantly improved AUCs to 0.782, 0.674, 0.734, and 0.831, respectively. The identified proteins were enriched in five clusters of AD-associated pathways including human immunodeficiency virus 1 infection, p53 signaling pathway, and phosphoinositide-3-kinase-protein kinase B/Akt signaling pathway. CONCLUSION Combined with age and APOE genotype, the proteins identified have the potential to serve as blood-based biomarkers for AD and await validation in future studies. While the NNs did not achieve better scores than the support vector machine model used in our previous study, their performances were likely limited by small sample size.
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Affiliation(s)
- Yuting Zhang
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Upamanyu Ghose
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Noel J. Buckley
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Sebastiaan Engelborghs
- Department of Biomedical Sciences, Reference Center for Biological Markers of Dementia (BIODEM), Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
- Department of Neurology, Universitair Ziekenhuis Brussel, Brussels, Belgium
- Center for Neurociences (C4N), Vrije Universiteit Brussel, Brussels, Belgium
| | - Kristel Sleegers
- Complex Genetics Group, VIB Center for Molecular Neurology, Antwerp, Belgium
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | | | - Anders Wallin
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
| | - Alberto Lleó
- Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
| | - Julius Popp
- Department of Psychiatry, University Hospital of Lausanne, Lausanne, Switzerland
- Department of Geriatric Psychiatry, University Hospital of Psychiatry and University of Zürich, Zürich, Switzerland
| | | | - Cristina Legido-Quigley
- Kings College London, London, United Kingdom
- The Systems Medicine Group, Steno Diabetes Center, Gentofte, Denmark
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, Netherlands
- University College London (UCL) Institutes of Neurology and Healthcare Engineering, London, United Kingdom
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- UK Dementia Research Institute at UCL, London, United Kingdom
- Department of Neurodegenerative Disease, UCL Institute of Neurology, London, United Kingdom
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong, China
| | - Pieter Jelle Visser
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Alzheimer Centrum Limburg, Maastricht University, Maastricht, Netherlands
- Alzheimer Center, VU University Medical Center, Amsterdam, Netherlands
| | - Lars Bertram
- Lübeck Interdisciplinary Platform for Genome Analytics, University of Lübeck, Lübeck, Germany
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Simon Lovestone
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
- Janssen R&D, High Wycombe, United Kingdom
| | | | - Liu Shi
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
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14
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Oppegaard KR, Armstrong TS, Anguera JA, Kober KM, Debr LK, Laister RC, Saligan LN, Ayala AP, Kuruvilla J, Alm MW, Byker WH, Miaskowski C, Mayo SJ. Blood-Based Biomarkers of Cancer-Related Cognitive Impairment in Non-Central Nervous System Cancer: A Scoping Review. Crit Rev Oncol Hematol 2022; 180:103822. [PMID: 36152911 DOI: 10.1016/j.critrevonc.2022.103822] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 09/17/2022] [Accepted: 09/19/2022] [Indexed: 11/24/2022] Open
Abstract
This scoping review was designed to synthesize the extant literature on associations between subjective and/or objective measures of cancer-related cognitive impairment (CRCI) and blood-based biomarkers in adults with non-central nervous system cancers. The literature search was done for studies published from the start of each database searched (i.e., MEDLINE, Embase, PsycINFO, Cumulative Index to Nursing and Allied Health Literature, Cochrane Central Register of Controlled Trials, grey literature) through to October 20, 2021. A total of 95 studies are included in this review. Of note, a wide variety of biomarkers were evaluated. Most studies evaluated patients with breast cancer. A variety of cognitive assessment measures were used. The most consistent significant findings were with various subjective and objective measures of CRCI and levels of interleukin-6 and tumor necrosis factor. Overall, biomarker research is in an exploratory phase. However, this review synthesizes findings and proposes directions for future research.
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Affiliation(s)
- Kate R Oppegaard
- University of California San Francisco, School of Nursing, Department of Physiological Nursing, USA
| | - Terri S Armstrong
- Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, USA
| | - Joaquin A Anguera
- University of California San Francisco, Department of Neurology and Psychiatry, USA
| | - Kord M Kober
- University of California San Francisco, School of Nursing, Department of Physiological Nursing, USA
| | - Lynch Kelly Debr
- University of Florida, College of Nursing, USA; University of Florida Health Cancer Center, USA
| | - Rob C Laister
- Princess Margaret Health Center, University Health Network, Canada
| | - Leorey N Saligan
- Symptoms Biology Unit, Division of Intramural Research, National Institutes of Health, USA
| | | | - John Kuruvilla
- Princess Margaret Health Center, University Health Network, Canada
| | - Mark W Alm
- Toronto General Hospital, University Health Network, Canada
| | | | - Christine Miaskowski
- University of California San Francisco, School of Medicine, Department of Anesthesia and Perioperative Care, USA
| | - Samantha J Mayo
- Lawrence S. Bloomberg Faculty of Nursing, University of Toronto, Canada.
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15
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Musigmann M, Akkurt BH, Krähling H, Nacul NG, Remonda L, Sartoretti T, Henssen D, Brokinkel B, Stummer W, Heindel W, Mannil M. Testing the applicability and performance of Auto ML for potential applications in diagnostic neuroradiology. Sci Rep 2022; 12:13648. [PMID: 35953588 PMCID: PMC9366823 DOI: 10.1038/s41598-022-18028-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Accepted: 08/03/2022] [Indexed: 11/25/2022] Open
Abstract
To investigate the applicability and performance of automated machine learning (AutoML) for potential applications in diagnostic neuroradiology. In the medical sector, there is a rapidly growing demand for machine learning methods, but only a limited number of corresponding experts. The comparatively simple handling of AutoML should enable even non-experts to develop adequate machine learning models with manageable effort. We aim to investigate the feasibility as well as the advantages and disadvantages of developing AutoML models compared to developing conventional machine learning models. We discuss the results in relation to a concrete example of a medical prediction application. In this retrospective IRB-approved study, a cohort of 107 patients who underwent gross total meningioma resection and a second cohort of 31 patients who underwent subtotal resection were included. Image segmentation of the contrast enhancing parts of the tumor was performed semi-automatically using the open-source software platform 3D Slicer. A total of 107 radiomic features were extracted by hand-delineated regions of interest from the pre-treatment MRI images of each patient. Within the AutoML approach, 20 different machine learning algorithms were trained and tested simultaneously. For comparison, a neural network and different conventional machine learning algorithms were trained and tested. With respect to the exemplary medical prediction application used in this study to evaluate the performance of Auto ML, namely the pre-treatment prediction of the achievable resection status of meningioma, AutoML achieved remarkable performance nearly equivalent to that of a feed-forward neural network with a single hidden layer. However, in the clinical case study considered here, logistic regression outperformed the AutoML algorithm. Using independent test data, we observed the following classification results (AutoML/neural network/logistic regression): mean area under the curve = 0.849/0.879/0.900, mean accuracy = 0.821/0.839/0.881, mean kappa = 0.465/0.491/0.644, mean sensitivity = 0.578/0.577/0.692 and mean specificity = 0.891/0.914/0.936. The results obtained with AutoML are therefore very promising. However, the AutoML models in our study did not yet show the corresponding performance of the best models obtained with conventional machine learning methods. While AutoML may facilitate and simplify the task of training and testing machine learning algorithms as applied in the field of neuroradiology and medical imaging, a considerable amount of expert knowledge may still be needed to develop models with the highest possible discriminatory power for diagnostic neuroradiology.
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Affiliation(s)
- Manfred Musigmann
- University Clinic for Radiology, Westfälische Wilhelms-University Muenster and University Hospital Münster, Albert-Schweitzer-Campus 1, 48149, Muenster, Germany
| | - Burak Han Akkurt
- University Clinic for Radiology, Westfälische Wilhelms-University Muenster and University Hospital Münster, Albert-Schweitzer-Campus 1, 48149, Muenster, Germany
| | - Hermann Krähling
- University Clinic for Radiology, Westfälische Wilhelms-University Muenster and University Hospital Münster, Albert-Schweitzer-Campus 1, 48149, Muenster, Germany
| | - Nabila Gala Nacul
- University Clinic for Radiology, Westfälische Wilhelms-University Muenster and University Hospital Münster, Albert-Schweitzer-Campus 1, 48149, Muenster, Germany
| | - Luca Remonda
- Institute of Neuroradiology, Kantonsspital Aarau, Aarau, Switzerland.,Faculty of Medicine, University of Bern, Bern, Switzerland
| | | | - Dylan Henssen
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Benjamin Brokinkel
- Department of Neurosurgery, Westfälische Wilhelms-University Muenster and University Hospital Muenster, Albert-Schweitzer-Campus 1, 48149, Muenster, Germany
| | - Walter Stummer
- Department of Neurosurgery, Westfälische Wilhelms-University Muenster and University Hospital Muenster, Albert-Schweitzer-Campus 1, 48149, Muenster, Germany
| | - Walter Heindel
- University Clinic for Radiology, Westfälische Wilhelms-University Muenster and University Hospital Münster, Albert-Schweitzer-Campus 1, 48149, Muenster, Germany
| | - Manoj Mannil
- University Clinic for Radiology, Westfälische Wilhelms-University Muenster and University Hospital Münster, Albert-Schweitzer-Campus 1, 48149, Muenster, Germany.
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16
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An Intelligent Learning System for Unbiased Prediction of Dementia Based on Autoencoder and Adaboost Ensemble Learning. LIFE (BASEL, SWITZERLAND) 2022; 12:life12071097. [PMID: 35888188 PMCID: PMC9318926 DOI: 10.3390/life12071097] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 06/22/2022] [Accepted: 06/28/2022] [Indexed: 11/17/2022]
Abstract
Dementia is a neurological condition that primarily affects older adults and there is still no cure or therapy available to cure it. The symptoms of dementia can appear as early as 10 years before the beginning of actual diagnosed dementia. Hence, machine learning (ML) researchers have presented several methods for early detection of dementia based on symptoms. However, these techniques suffer from two major flaws. The first issue is the bias of ML models caused by imbalanced classes in the dataset. Past research did not address this issue well and did not take preventative precautions. Different ML models were developed to illustrate this bias. To alleviate the problem of bias, we deployed a synthetic minority oversampling technique (SMOTE) to balance the training process of the proposed ML model. The second issue is the poor classification accuracy of ML models, which leads to a limited clinical significance. To improve dementia prediction accuracy, we proposed an intelligent learning system that is a hybrid of an autoencoder and adaptive boost model. The autoencoder is used to extract relevant features from the feature space and the Adaboost model is deployed for the classification of dementia by using an extracted subset of features. The hyperparameters of the Adaboost model are fine-tuned using a grid search algorithm. Experimental findings reveal that the suggested learning system outperforms eleven similar systems which were proposed in the literature. Furthermore, it was also observed that the proposed learning system improves the strength of the conventional Adaboost model by 9.8% and reduces its time complexity. Lastly, the proposed learning system achieved classification accuracy of 90.23%, sensitivity of 98.00% and specificity of 96.65%.
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17
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Xiao S, Tian H, Tao P. PASSer2.0: Accurate Prediction of Protein Allosteric Sites Through Automated Machine Learning. Front Mol Biosci 2022; 9:879251. [PMID: 35898310 PMCID: PMC9309527 DOI: 10.3389/fmolb.2022.879251] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Accepted: 05/23/2022] [Indexed: 11/16/2022] Open
Abstract
Allostery is a fundamental process in regulating protein activities. The discovery, design, and development of allosteric drugs demand better identification of allosteric sites. Several computational methods have been developed previously to predict allosteric sites using static pocket features and protein dynamics. Here, we define a baseline model for allosteric site prediction and present a computational model using automated machine learning. Our model, PASSer2.0, advanced the previous results and performed well across multiple indicators with 82.7% of allosteric pockets appearing among the top three positions. The trained machine learning model has been integrated with the Protein Allosteric Sites Server (PASSer) to facilitate allosteric drug discovery.
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Affiliation(s)
| | - Hao Tian
- Center for Research Computing, Center for Drug Discovery, Design and Delivery (CD4), Department of Chemistry, Southern Methodist University, Dallas, TX, United States
| | - Peng Tao
- Center for Research Computing, Center for Drug Discovery, Design and Delivery (CD4), Department of Chemistry, Southern Methodist University, Dallas, TX, United States
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18
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Just Add Data: automated predictive modeling for knowledge discovery and feature selection. NPJ Precis Oncol 2022; 6:38. [PMID: 35710826 PMCID: PMC9203777 DOI: 10.1038/s41698-022-00274-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Accepted: 04/13/2022] [Indexed: 01/20/2023] Open
Abstract
Fully automated machine learning (AutoML) for predictive modeling is becoming a reality, giving rise to a whole new field. We present the basic ideas and principles of Just Add Data Bio (JADBio), an AutoML platform applicable to the low-sample, high-dimensional omics data that arise in translational medicine and bioinformatics applications. In addition to predictive and diagnostic models ready for clinical use, JADBio focuses on knowledge discovery by performing feature selection and identifying the corresponding biosignatures, i.e., minimal-size subsets of biomarkers that are jointly predictive of the outcome or phenotype of interest. It also returns a palette of useful information for interpretation, clinical use of the models, and decision making. JADBio is qualitatively and quantitatively compared against Hyper-Parameter Optimization Machine Learning libraries. Results show that in typical omics dataset analysis, JADBio manages to identify signatures comprising of just a handful of features while maintaining competitive predictive performance and accurate out-of-sample performance estimation.
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19
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Varesi A, Carrara A, Pires VG, Floris V, Pierella E, Savioli G, Prasad S, Esposito C, Ricevuti G, Chirumbolo S, Pascale A. Blood-Based Biomarkers for Alzheimer's Disease Diagnosis and Progression: An Overview. Cells 2022; 11:1367. [PMID: 35456047 PMCID: PMC9044750 DOI: 10.3390/cells11081367] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 04/12/2022] [Accepted: 04/15/2022] [Indexed: 01/10/2023] Open
Abstract
Alzheimer's Disease (AD) is a progressive neurodegenerative disease characterized by amyloid-β (Aβ) plaque deposition and neurofibrillary tangle accumulation in the brain. Although several studies have been conducted to unravel the complex and interconnected pathophysiology of AD, clinical trial failure rates have been high, and no disease-modifying therapies are presently available. Fluid biomarker discovery for AD is a rapidly expanding field of research aimed at anticipating disease diagnosis and following disease progression over time. Currently, Aβ1-42, phosphorylated tau, and total tau levels in the cerebrospinal fluid are the best-studied fluid biomarkers for AD, but the need for novel, cheap, less-invasive, easily detectable, and more-accessible markers has recently led to the search for new blood-based molecules. However, despite considerable research activity, a comprehensive and up-to-date overview of the main blood-based biomarker candidates is still lacking. In this narrative review, we discuss the role of proteins, lipids, metabolites, oxidative-stress-related molecules, and cytokines as possible disease biomarkers. Furthermore, we highlight the potential of the emerging miRNAs and long non-coding RNAs (lncRNAs) as diagnostic tools, and we briefly present the role of vitamins and gut-microbiome-related molecules as novel candidates for AD detection and monitoring, thus offering new insights into the diagnosis and progression of this devastating disease.
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Affiliation(s)
- Angelica Varesi
- Department of Biology and Biotechnology, University of Pavia, 27100 Pavia, Italy
- Almo Collegio Borromeo, 27100 Pavia, Italy
| | - Adelaide Carrara
- Department of Internal Medicine and Therapeutics, University of Pavia, 27100 Pavia, Italy; (A.C.); (V.F.)
| | - Vitor Gomes Pires
- Department of Biological Sciences, Columbia University, New York, NY 10027, USA;
| | - Valentina Floris
- Department of Internal Medicine and Therapeutics, University of Pavia, 27100 Pavia, Italy; (A.C.); (V.F.)
| | - Elisa Pierella
- School of Medicine, Faculty of Clinical and Biomedical Sciences, University of Central Lancashire, Preston PR1 2HE, UK;
| | - Gabriele Savioli
- Emergency Department, IRCCS Policlinico San Matteo, 27100 Pavia, Italy;
| | - Sakshi Prasad
- Faculty of Medicine, National Pirogov Memorial Medical University, 21018 Vinnytsya, Ukraine;
| | - Ciro Esposito
- Unit of Nephrology and Dialysis, ICS Maugeri, University of Pavia, 27100 Pavia, Italy;
| | - Giovanni Ricevuti
- Department of Drug Sciences, University of Pavia, 27100 Pavia, Italy
| | - Salvatore Chirumbolo
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, 37129 Verona, Italy;
| | - Alessia Pascale
- Department of Drug Sciences, Section of Pharmacology, University of Pavia, 27100 Pavia, Italy;
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20
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Karaglani M, Panagopoulou M, Baltsavia I, Apalaki P, Theodosiou T, Iliopoulos I, Tsamardinos I, Chatzaki E. Tissue-Specific Methylation Biosignatures for Monitoring Diseases: An In Silico Approach. Int J Mol Sci 2022; 23:2959. [PMID: 35328380 PMCID: PMC8952417 DOI: 10.3390/ijms23062959] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 03/01/2022] [Accepted: 03/03/2022] [Indexed: 02/06/2023] Open
Abstract
Tissue-specific gene methylation events are key to the pathogenesis of several diseases and can be utilized for diagnosis and monitoring. Here, we established an in silico pipeline to analyze high-throughput methylome datasets to identify specific methylation fingerprints in three pathological entities of major burden, i.e., breast cancer (BrCa), osteoarthritis (OA) and diabetes mellitus (DM). Differential methylation analysis was conducted to compare tissues/cells related to the pathology and different types of healthy tissues, revealing Differentially Methylated Genes (DMGs). Highly performing and low feature number biosignatures were built with automated machine learning, including: (1) a five-gene biosignature discriminating BrCa tissue from healthy tissues (AUC 0.987 and precision 0.987), (2) three equivalent OA cartilage-specific biosignatures containing four genes each (AUC 0.978 and precision 0.986) and (3) a four-gene pancreatic β-cell-specific biosignature (AUC 0.984 and precision 0.995). Next, the BrCa biosignature was validated using an independent ccfDNA dataset showing an AUC and precision of 1.000, verifying the biosignature's applicability in liquid biopsy. Functional and protein interaction prediction analysis revealed that most DMGs identified are involved in pathways known to be related to the studied diseases or pointed to new ones. Overall, our data-driven approach contributes to the maximum exploitation of high-throughput methylome readings, helping to establish specific disease profiles to be applied in clinical practice and to understand human pathology.
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Affiliation(s)
- Makrina Karaglani
- Laboratory of Pharmacology, Medical School, Democritus University of Thrace, GR-68100 Alexandroupolis, Greece; (M.K.); (M.P.); (P.A.); (T.T.)
| | - Maria Panagopoulou
- Laboratory of Pharmacology, Medical School, Democritus University of Thrace, GR-68100 Alexandroupolis, Greece; (M.K.); (M.P.); (P.A.); (T.T.)
| | - Ismini Baltsavia
- Department of Basic Sciences, School of Medicine, University of Crete, GR-71003 Heraklion, Greece; (I.B.); (I.I.)
| | - Paraskevi Apalaki
- Laboratory of Pharmacology, Medical School, Democritus University of Thrace, GR-68100 Alexandroupolis, Greece; (M.K.); (M.P.); (P.A.); (T.T.)
| | - Theodosis Theodosiou
- Laboratory of Pharmacology, Medical School, Democritus University of Thrace, GR-68100 Alexandroupolis, Greece; (M.K.); (M.P.); (P.A.); (T.T.)
| | - Ioannis Iliopoulos
- Department of Basic Sciences, School of Medicine, University of Crete, GR-71003 Heraklion, Greece; (I.B.); (I.I.)
| | - Ioannis Tsamardinos
- JADBio Gnosis DA S.A., Science and Technology Park of Crete, GR-70013 Heraklion, Greece;
- Department of Computer Science, University of Crete, GR-70013 Heraklion, Greece
- Institute of Applied and Computational Mathematics, Foundation for Research and Technology—Hellas, GR-70013 Heraklion, Greece
| | - Ekaterini Chatzaki
- Laboratory of Pharmacology, Medical School, Democritus University of Thrace, GR-68100 Alexandroupolis, Greece; (M.K.); (M.P.); (P.A.); (T.T.)
- Institute of Agri-Food and Life Sciences, Hellenic Mediterranean University Research Centre, GR-71410 Heraklion, Greece
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21
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Karaglani M, Panagopoulou M, Cheimonidi C, Tsamardinos I, Maltezos E, Papanas N, Papazoglou D, Mastorakos G, Chatzaki E. Liquid Biopsy in Type 2 Diabetes Mellitus Management: Building Specific Biosignatures via Machine Learning. J Clin Med 2022; 11:1045. [PMID: 35207316 PMCID: PMC8876363 DOI: 10.3390/jcm11041045] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 02/09/2022] [Accepted: 02/15/2022] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND The need for minimally invasive biomarkers for the early diagnosis of type 2 diabetes (T2DM) prior to the clinical onset and monitoring of β-pancreatic cell loss is emerging. Here, we focused on studying circulating cell-free DNA (ccfDNA) as a liquid biopsy biomaterial for accurate diagnosis/monitoring of T2DM. METHODS ccfDNA levels were directly quantified in sera from 96 T2DM patients and 71 healthy individuals via fluorometry, and then fragment DNA size profiling was performed by capillary electrophoresis. Following this, ccfDNA methylation levels of five β-cell-related genes were measured via qPCR. Data were analyzed by automated machine learning to build classifying predictive models. RESULTS ccfDNA levels were found to be similar between groups but indicative of apoptosis in T2DM. INS (Insulin), IAPP (Islet Amyloid Polypeptide-Amylin), GCK (Glucokinase), and KCNJ11 (Potassium Inwardly Rectifying Channel Subfamily J member 11) levels differed significantly between groups. AutoML analysis delivered biosignatures including GCK, IAPP and KCNJ11 methylation, with the highest ever reported discriminating performance of T2DM from healthy individuals (AUC 0.927). CONCLUSIONS Our data unravel the value of ccfDNA as a minimally invasive biomaterial carrying important clinical information for T2DM. Upon prospective clinical evaluation, the built biosignature can be disruptive for T2DM clinical management.
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Affiliation(s)
- Makrina Karaglani
- Laboratory of Pharmacology, Department of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece; (M.K.); (M.P.); (C.C.)
| | - Maria Panagopoulou
- Laboratory of Pharmacology, Department of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece; (M.K.); (M.P.); (C.C.)
| | - Christina Cheimonidi
- Laboratory of Pharmacology, Department of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece; (M.K.); (M.P.); (C.C.)
| | - Ioannis Tsamardinos
- JADBio Gnosis DA, Science and Technology Park of Crete, 71500 Heraklion, Greece;
| | - Efstratios Maltezos
- Diabetes Centre, 2nd Department of Internal Medicine, Democritus University of Thrace, University Hospital of Alexandroupolis, 68100 Alexandroupolis, Greece; (E.M.); (N.P.); (D.P.)
| | - Nikolaos Papanas
- Diabetes Centre, 2nd Department of Internal Medicine, Democritus University of Thrace, University Hospital of Alexandroupolis, 68100 Alexandroupolis, Greece; (E.M.); (N.P.); (D.P.)
| | - Dimitrios Papazoglou
- Diabetes Centre, 2nd Department of Internal Medicine, Democritus University of Thrace, University Hospital of Alexandroupolis, 68100 Alexandroupolis, Greece; (E.M.); (N.P.); (D.P.)
| | - George Mastorakos
- Endocrine Unit, 2nd Department of Obstetrics and Gynecology, National and Kapodistrian University of Athens, “Aretaieion” University Hospital, 11528 Athens, Greece;
| | - Ekaterini Chatzaki
- Laboratory of Pharmacology, Department of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece; (M.K.); (M.P.); (C.C.)
- Institute of Agri-Food and Life Sciences, Hellenic Mediterranean University Research Centre, 71003 Heraklion, Greece
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22
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Li Z, Jiang X, Wang Y, Kim Y. Applied machine learning in Alzheimer's disease research: omics, imaging, and clinical data. Emerg Top Life Sci 2021; 5:765-777. [PMID: 34881778 PMCID: PMC8786302 DOI: 10.1042/etls20210249] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 11/05/2021] [Accepted: 11/17/2021] [Indexed: 01/26/2023]
Abstract
Alzheimer's disease (AD) remains a devastating neurodegenerative disease with few preventive or curative treatments available. Modern technology developments of high-throughput omics platforms and imaging equipment provide unprecedented opportunities to study the etiology and progression of this disease. Meanwhile, the vast amount of data from various modalities, such as genetics, proteomics, transcriptomics, and imaging, as well as clinical features impose great challenges in data integration and analysis. Machine learning (ML) methods offer novel techniques to address high dimensional data, integrate data from different sources, model the etiological and clinical heterogeneity, and discover new biomarkers. These directions have the potential to help us better manage the disease progression and develop novel treatment strategies. This mini-review paper summarizes different ML methods that have been applied to study AD using single-platform or multi-modal data. We review the current state of ML applications for five key directions of AD research: disease classification, drug repurposing, subtyping, progression prediction, and biomarker discovery. This summary provides insights about the current research status of ML-based AD research and highlights potential directions for future research.
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Affiliation(s)
- Ziyi Li
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, U.S.A
| | - Xiaoqian Jiang
- School of Biomedical Informatics, The University of Texas Health Science Center, Houston, TX, U.S.A
| | - Yizhuo Wang
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, U.S.A
| | - Yejin Kim
- School of Biomedical Informatics, The University of Texas Health Science Center, Houston, TX, U.S.A
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23
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Papoutsoglou G, Karaglani M, Lagani V, Thomson N, Røe OD, Tsamardinos I, Chatzaki E. Automated machine learning optimizes and accelerates predictive modeling from COVID-19 high throughput datasets. Sci Rep 2021; 11:15107. [PMID: 34302024 PMCID: PMC8302755 DOI: 10.1038/s41598-021-94501-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 07/08/2021] [Indexed: 12/24/2022] Open
Abstract
COVID-19 outbreak brings intense pressure on healthcare systems, with an urgent demand for effective diagnostic, prognostic and therapeutic procedures. Here, we employed Automated Machine Learning (AutoML) to analyze three publicly available high throughput COVID-19 datasets, including proteomic, metabolomic and transcriptomic measurements. Pathway analysis of the selected features was also performed. Analysis of a combined proteomic and metabolomic dataset led to 10 equivalent signatures of two features each, with AUC 0.840 (CI 0.723-0.941) in discriminating severe from non-severe COVID-19 patients. A transcriptomic dataset led to two equivalent signatures of eight features each, with AUC 0.914 (CI 0.865-0.955) in identifying COVID-19 patients from those with a different acute respiratory illness. Another transcriptomic dataset led to two equivalent signatures of nine features each, with AUC 0.967 (CI 0.899-0.996) in identifying COVID-19 patients from virus-free individuals. Signature predictive performance remained high upon validation. Multiple new features emerged and pathway analysis revealed biological relevance by implication in Viral mRNA Translation, Interferon gamma signaling and Innate Immune System pathways. In conclusion, AutoML analysis led to multiple biosignatures of high predictive performance, with reduced features and large choice of alternative predictors. These favorable characteristics are eminent for development of cost-effective assays to contribute to better disease management.
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Affiliation(s)
- Georgios Papoutsoglou
- JADBio, Gnosis Data Analysis PC, Science and Technology Park of Crete, N. Plastira 100, Vassilika Vouton, 70013, Heraklion, Crete, Greece
- Computer Science Department, University of Crete, Voutes Campus, 70013, Heraklion, Crete, Greece
| | - Makrina Karaglani
- JADBio, Gnosis Data Analysis PC, Science and Technology Park of Crete, N. Plastira 100, Vassilika Vouton, 70013, Heraklion, Crete, Greece
- Laboratory of Pharmacology, Medical School, Democritus University of Thrace, 68100, Alexandroupolis, Greece
| | - Vincenzo Lagani
- JADBio, Gnosis Data Analysis PC, Science and Technology Park of Crete, N. Plastira 100, Vassilika Vouton, 70013, Heraklion, Crete, Greece
- Institute of Chemical Biology, Ilia State University, Kakutsa Cholokashvili Ave 3/5, 0162, Tbilisi, Georgia
| | - Naomi Thomson
- JADBio, Gnosis Data Analysis PC, Science and Technology Park of Crete, N. Plastira 100, Vassilika Vouton, 70013, Heraklion, Crete, Greece
| | - Oluf Dimitri Røe
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Prinsesse Kristinsgt. 1, 7491, Trondheim, Norway
- Clinical Cancer Research Center, Department of Clinical Medicine, Aalborg University Hospital, Hobrovej 18-22, 9100, Aalborg, Denmark
| | - Ioannis Tsamardinos
- JADBio, Gnosis Data Analysis PC, Science and Technology Park of Crete, N. Plastira 100, Vassilika Vouton, 70013, Heraklion, Crete, Greece
- Computer Science Department, University of Crete, Voutes Campus, 70013, Heraklion, Crete, Greece
| | - Ekaterini Chatzaki
- Laboratory of Pharmacology, Medical School, Democritus University of Thrace, 68100, Alexandroupolis, Greece.
- Institute of Agri-Food and Life Sciences, Mediterranean University Research Centre, 71410, Heraklion, Crete, Greece.
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24
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Panagopoulou M, Cheretaki A, Karaglani M, Balgkouranidou I, Biziota E, Amarantidis K, Xenidis N, Kakolyris S, Baritaki S, Chatzaki E. Methylation Status of Corticotropin-Releasing Factor (CRF) Receptor Genes in Colorectal Cancer. J Clin Med 2021; 10:2680. [PMID: 34207031 PMCID: PMC8234503 DOI: 10.3390/jcm10122680] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 06/13/2021] [Accepted: 06/15/2021] [Indexed: 02/07/2023] Open
Abstract
The corticotropin-releasing factor (CRF) system has been strongly associated with gastrointestinal pathophysiology, including colorectal cancer (CRC). We previously showed that altered expression of CRF receptors (CRFRs) in the colon critically affects CRC progression and aggressiveness through regulation of colonic inflammation. Here, we aimed to assess the potential of CRFR methylation levels as putative biomarkers in CRC. In silico methylation analysis of CRF receptor 1 (CRFR1) and CRF receptor 2 (CRFR2) was performed using methylome data derived by CRC and Crohn's disease (CD) tissues and CRC-derived circulating cell-free DNAs (ccfDNAs). In total, 32 and 33 differentially methylated sites of CpGs (DMCs) emerged in CRFR1 and CRFR2, respectively, between healthy and diseased tissues. The methylation patterns were verified in patient-derived ccfDNA samples by qMSP and associated with clinicopathological characteristics. An automated machine learning (AutoML) technology was applied to ccfDNA samples for classification analysis. In silico analysis revealed increased methylation of both CRFRs in CRC tissue and ccfDNA-derived datasets. CRFR1 hypermethylation was also noticed in gene body DMCs of CD patients. CRFR1 hypermethylation was further validated in CRC adjuvant-derived ccfDNA samples, whereas CRFR1 hypomethylation, observed in metastasis-derived ccfDNAs, was correlated to disease aggressiveness and adverse prognostic characteristics. AutoML analysis based on CRFRs methylation status revealed a three-feature high-performing biosignature for CRC diagnosis with an estimated AUC of 0.929. Monitoring of CRFRs methylation-based signature in CRC tissues and ccfDNAs may be of high diagnostic and prognostic significance in CRC.
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Affiliation(s)
- Maria Panagopoulou
- Laboratory of Pharmacology, Medical School, Democritus University of Thrace, GR-68100 Alexandroupolis, Greece; (M.P.); (A.C.); (M.K.); (I.B.)
| | - Antonia Cheretaki
- Laboratory of Pharmacology, Medical School, Democritus University of Thrace, GR-68100 Alexandroupolis, Greece; (M.P.); (A.C.); (M.K.); (I.B.)
| | - Makrina Karaglani
- Laboratory of Pharmacology, Medical School, Democritus University of Thrace, GR-68100 Alexandroupolis, Greece; (M.P.); (A.C.); (M.K.); (I.B.)
| | - Ioanna Balgkouranidou
- Laboratory of Pharmacology, Medical School, Democritus University of Thrace, GR-68100 Alexandroupolis, Greece; (M.P.); (A.C.); (M.K.); (I.B.)
- Department of Medical Oncology, Medical School, Democritus University of Thrace, GR-68100 Alexandroupolis, Greece; (E.B.); (K.A.); (N.X.); (S.K.)
| | - Eirini Biziota
- Department of Medical Oncology, Medical School, Democritus University of Thrace, GR-68100 Alexandroupolis, Greece; (E.B.); (K.A.); (N.X.); (S.K.)
| | - Kyriakos Amarantidis
- Department of Medical Oncology, Medical School, Democritus University of Thrace, GR-68100 Alexandroupolis, Greece; (E.B.); (K.A.); (N.X.); (S.K.)
| | - Nikolaos Xenidis
- Department of Medical Oncology, Medical School, Democritus University of Thrace, GR-68100 Alexandroupolis, Greece; (E.B.); (K.A.); (N.X.); (S.K.)
| | - Stylianos Kakolyris
- Department of Medical Oncology, Medical School, Democritus University of Thrace, GR-68100 Alexandroupolis, Greece; (E.B.); (K.A.); (N.X.); (S.K.)
| | - Stavroula Baritaki
- Laboratory of Experimental Oncology, Division of Surgery, School of Medicine, University of Crete, GR-71003 Heraklion, Greece
| | - Ekaterini Chatzaki
- Laboratory of Pharmacology, Medical School, Democritus University of Thrace, GR-68100 Alexandroupolis, Greece; (M.P.); (A.C.); (M.K.); (I.B.)
- Hellenic Mediterranean University Research Centre, Institute of Agri-Food and Life Sciences, GR-71410 Heraklion, Greece
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25
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Panagopoulou M, Karaglani M, Manolopoulos VG, Iliopoulos I, Tsamardinos I, Chatzaki E. Deciphering the Methylation Landscape in Breast Cancer: Diagnostic and Prognostic Biosignatures through Automated Machine Learning. Cancers (Basel) 2021; 13:1677. [PMID: 33918195 PMCID: PMC8037759 DOI: 10.3390/cancers13071677] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 03/23/2021] [Accepted: 03/31/2021] [Indexed: 12/24/2022] Open
Abstract
DNA methylation plays an important role in breast cancer (BrCa) pathogenesis and could contribute to driving its personalized management. We performed a complete bioinformatic analysis in BrCa whole methylome datasets, analyzed using the Illumina methylation 450 bead-chip array. Differential methylation analysis vs. clinical end-points resulted in 11,176 to 27,786 differentially methylated genes (DMGs). Innovative automated machine learning (AutoML) was employed to construct signatures with translational value. Three highly performing and low-feature-number signatures were built: (1) A 5-gene signature discriminating BrCa patients from healthy individuals (area under the curve (AUC): 0.994 (0.982-1.000)). (2) A 3-gene signature identifying BrCa metastatic disease (AUC: 0.986 (0.921-1.000)). (3) Six equivalent 5-gene signatures diagnosing early disease (AUC: 0.973 (0.920-1.000)). Validation in independent patient groups verified performance. Bioinformatic tools for functional analysis and protein interaction prediction were also employed. All protein encoding features included in the signatures were associated with BrCa-related pathways. Functional analysis of DMGs highlighted the regulation of transcription as the main biological process, the nucleus as the main cellular component and transcription factor activity and sequence-specific DNA binding as the main molecular functions. Overall, three high-performance diagnostic/prognostic signatures were built and are readily available for improving BrCa precision management upon prospective clinical validation. Revisiting archived methylomes through novel bioinformatic approaches revealed significant clarifying knowledge for the contribution of gene methylation events in breast carcinogenesis.
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Affiliation(s)
- Maria Panagopoulou
- Laboratory of Pharmacology, Medical School, Democritus University of Thrace, GR-68100 Alexandroupolis, Greece; (M.P.); (M.K.); (V.G.M.)
| | - Makrina Karaglani
- Laboratory of Pharmacology, Medical School, Democritus University of Thrace, GR-68100 Alexandroupolis, Greece; (M.P.); (M.K.); (V.G.M.)
| | - Vangelis G. Manolopoulos
- Laboratory of Pharmacology, Medical School, Democritus University of Thrace, GR-68100 Alexandroupolis, Greece; (M.P.); (M.K.); (V.G.M.)
| | - Ioannis Iliopoulos
- Department of Basic Sciences, School of Medicine, University of Crete, GR-71003 Heraklion, Greece;
| | - Ioannis Tsamardinos
- JADBio, Gnosis Data Analysis PC, Science and Technology Park of Crete, GR-70013 Heraklion, Greece;
- Department of Computer Science, University of Crete, GR-70013 Heraklion, Greece
- Institute of Applied and Computational Mathematics, Foundation for Research and Technology–Hellas, GR-70013 Heraklion, Greece
| | - Ekaterini Chatzaki
- Laboratory of Pharmacology, Medical School, Democritus University of Thrace, GR-68100 Alexandroupolis, Greece; (M.P.); (M.K.); (V.G.M.)
- Institute of Agri-Food and Life Sciences, Hellenic Mediterranean University Research Centre, GR-71410 Heraklion, Greece
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Abstract
Machine learning (ML) has been slowly entering every aspect of our lives and its positive impact has been astonishing. To accelerate embedding ML in more applications and incorporating it in real-world scenarios, automated machine learning (AutoML) is emerging. The main purpose of AutoML is to provide seamless integration of ML in various industries, which will facilitate better outcomes in everyday tasks. In healthcare, AutoML has been already applied to easier settings with structured data such as tabular lab data. However, there is still a need for applying AutoML for interpreting medical text, which is being generated at a tremendous rate. For this to happen, a promising method is AutoML for clinical notes analysis, which is an unexplored research area representing a gap in ML research. The main objective of this paper is to fill this gap and provide a comprehensive survey and analytical study towards AutoML for clinical notes. To that end, we first introduce the AutoML technology and review its various tools and techniques. We then survey the literature of AutoML in the healthcare industry and discuss the developments specific to clinical settings, as well as those using general AutoML tools for healthcare applications. With this background, we then discuss challenges of working with clinical notes and highlight the benefits of developing AutoML for medical notes processing. Next, we survey relevant ML research for clinical notes and analyze the literature and the field of AutoML in the healthcare industry. Furthermore, we propose future research directions and shed light on the challenges and opportunities this emerging field holds. With this, we aim to assist the community with the implementation of an AutoML platform for medical notes, which if realized can revolutionize patient outcomes.
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