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Teixeira M, Silva F, Ferreira RM, Pereira T, Figueiredo C, Oliveira HP. A review of machine learning methods for cancer characterization from microbiome data. NPJ Precis Oncol 2024; 8:123. [PMID: 38816569 PMCID: PMC11139966 DOI: 10.1038/s41698-024-00617-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Accepted: 05/17/2024] [Indexed: 06/01/2024] Open
Abstract
Recent studies have shown that the microbiome can impact cancer development, progression, and response to therapies suggesting microbiome-based approaches for cancer characterization. As cancer-related signatures are complex and implicate many taxa, their discovery often requires Machine Learning approaches. This review discusses Machine Learning methods for cancer characterization from microbiome data. It focuses on the implications of choices undertaken during sample collection, feature selection and pre-processing. It also discusses ML model selection, guiding how to choose an ML model, and model validation. Finally, it enumerates current limitations and how these may be surpassed. Proposed methods, often based on Random Forests, show promising results, however insufficient for widespread clinical usage. Studies often report conflicting results mainly due to ML models with poor generalizability. We expect that evaluating models with expanded, hold-out datasets, removing technical artifacts, exploring representations of the microbiome other than taxonomical profiles, leveraging advances in deep learning, and developing ML models better adapted to the characteristics of microbiome data will improve the performance and generalizability of models and enable their usage in the clinic.
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Affiliation(s)
- Marco Teixeira
- Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal.
- Faculty of Engineering, University of Porto, Porto, Portugal.
| | - Francisco Silva
- Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal
- Faculty of Science, University of Porto, Porto, Portugal
| | - Rui M Ferreira
- Ipatimup - Institute of Molecular Pathology and Immunology of the University of Porto, Porto, Portugal
- Instituto de Investigação e Inovação em Saúde, University of Porto, Porto, Portugal
| | - Tania Pereira
- Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal
- Faculty of Sciences and Technology, University of Coimbra, Coimbra, Portugal
| | - Ceu Figueiredo
- Ipatimup - Institute of Molecular Pathology and Immunology of the University of Porto, Porto, Portugal
- Instituto de Investigação e Inovação em Saúde, University of Porto, Porto, Portugal
- Faculty of Medicine, University of Porto, Porto, Portugal
| | - Hélder P Oliveira
- Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal
- Faculty of Science, University of Porto, Porto, Portugal
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Annesley SJ, Missailidis D, Heng B, Josev EK, Armstrong CW. Unravelling shared mechanisms: insights from recent ME/CFS research to illuminate long COVID pathologies. Trends Mol Med 2024; 30:443-458. [PMID: 38443223 DOI: 10.1016/j.molmed.2024.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 01/29/2024] [Accepted: 02/13/2024] [Indexed: 03/07/2024]
Abstract
Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating chronic illness often triggered by an initiating acute event, mainly viral infections. The transition from acute to chronic disease remains unknown, but interest in this phenomenon has escalated since the COVID-19 pandemic and the post-COVID-19 illness, termed 'long COVID' (LC). Both ME/CFS and LC share many clinical similarities. Here, we present recent findings in ME/CFS research focussing on proposed disease pathologies shared with LC. Understanding these disease pathologies and how they influence each other is key to developing effective therapeutics and diagnostic tests. Given that ME/CFS typically has a longer disease duration compared with LC, with symptoms and pathologies evolving over time, ME/CFS may provide insights into the future progression of LC.
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Affiliation(s)
- Sarah J Annesley
- Department of Microbiology, Anatomy, Physiology and Pharmacology, La Trobe University, VIC, Australia.
| | - Daniel Missailidis
- Department of Microbiology, Anatomy, Physiology and Pharmacology, La Trobe University, VIC, Australia
| | - Benjamin Heng
- Macquarie Medical School, Faculty of Medicine, Human and Health Sciences, Macquarie University, Sydney, NSW, Australia
| | - Elisha K Josev
- Neurodisability & Rehabilitation, Clinical Sciences, Murdoch Children's Research Institute, Parkville, VIC, Australia; Department of Paediatrics, University of Melbourne, Royal Children's Hospital, Parkville, VIC, Australia; Mercy Hospital for Women, Heidelberg, VIC, Australia
| | - Christopher W Armstrong
- Department of Biochemistry and Pharmacology, Bio21 Molecular Science and Biotechnology Institute, University of Melbourne, Parkville, VIC, Australia
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Feng J, Yang K, Liu X, Song M, Zhan P, Zhang M, Chen J, Liu J. Machine learning: a powerful tool for identifying key microbial agents associated with specific cancer types. PeerJ 2023; 11:e16304. [PMID: 37901464 PMCID: PMC10601900 DOI: 10.7717/peerj.16304] [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: 02/08/2023] [Accepted: 09/26/2023] [Indexed: 10/31/2023] Open
Abstract
Machine learning (ML) includes a broad class of computer programs that improve with experience and shows unique strengths in performing tasks such as clustering, classification and regression. Over the past decade, microbial communities have been implicated in influencing the onset, progression, metastasis, and therapeutic response of multiple cancers. Host-microbe interaction may be a physiological pathway contributing to cancer development. With the accumulation of a large number of high-throughput data, ML has been successfully applied to the study of human cancer microbiomics in an attempt to reveal the complex mechanism behind cancer. In this review, we begin with a brief overview of the data sources included in cancer microbiomics studies. Then, the characteristics of the ML algorithm are briefly introduced. Secondly, the application progress of ML in cancer microbiomics is also reviewed. Finally, we highlight the challenges and future prospects facing ML in cancer microbiomics. On this basis, we conclude that the development of cancer microbiomics can not be achieved without ML, and that ML can be used to develop tumor-targeting microbial therapies, ultimately contributing to personalized and precision medicine.
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Affiliation(s)
- Jia Feng
- Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, Sichuan, China
| | - Kailan Yang
- Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, Sichuan, China
| | - Xuexue Liu
- Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, Sichuan, China
| | - Min Song
- Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, Sichuan, China
| | - Ping Zhan
- Department of Obstetrics, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China
| | - Mi Zhang
- Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, Sichuan, China
| | - Jinsong Chen
- Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, Sichuan, China
| | - Jinbo Liu
- Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, Sichuan, China
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Vázquez-Marrufo M, Sarrias-Arrabal E, García-Torres M, Martín-Clemente R, Izquierdo G. A systematic review of the application of machine-learning algorithms in multiple sclerosis. Neurologia 2023; 38:577-590. [PMID: 35843587 DOI: 10.1016/j.nrleng.2020.10.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Accepted: 10/11/2020] [Indexed: 10/17/2022] Open
Abstract
INTRODUCTION The applications of artificial intelligence, and in particular automatic learning or "machine learning" (ML), constitute both a challenge and a great opportunity in numerous scientific, technical, and clinical disciplines. Specific applications in the study of multiple sclerosis (MS) have been no exception, and constitute an area of increasing interest in recent years. OBJECTIVE We present a systematic review of the application of ML algorithms in MS. MATERIALS AND METHODS We used the PubMed search engine, which allows free access to the MEDLINE medical database, to identify studies including the keywords "machine learning" and "multiple sclerosis." We excluded review articles, studies written in languages other than English or Spanish, and studies that were mainly technical and did not specifically apply to MS. The final selection included 76 articles, and 38 were rejected. CONCLUSIONS After the review process, we established 4 main applications of ML in MS: 1) classifying MS subtypes; 2) distinguishing patients with MS from healthy controls and individuals with other diseases; 3) predicting progression and response to therapeutic interventions; and 4) other applications. Results found to date have shown that ML algorithms may offer great support for health professionals both in clinical settings and in research into MS.
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Affiliation(s)
- M Vázquez-Marrufo
- Departamento de Psicología Experimental, Facultad de Psicología, Universidad de Sevilla, Sevilla, Spain.
| | - E Sarrias-Arrabal
- Departamento de Psicología Experimental, Facultad de Psicología, Universidad de Sevilla, Sevilla, Spain
| | - M García-Torres
- Escuela Politécnica Superior, Universidad Pablo de Olavide, Sevilla, Spain
| | - R Martín-Clemente
- Departamento de Teoría de la Señal y Comunicaciones, Escuela Técnica Superior de Ingeniería, Universidad de Sevilla, Sevilla, Spain
| | - G Izquierdo
- Unidad de Esclerosis Múltiple, Hospital VITHAS, Sevilla, Spain
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Jiang S, Wang T, Zhang KH. Data-driven decision-making for precision diagnosis of digestive diseases. Biomed Eng Online 2023; 22:87. [PMID: 37658345 PMCID: PMC10472739 DOI: 10.1186/s12938-023-01148-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Accepted: 08/15/2023] [Indexed: 09/03/2023] Open
Abstract
Modern omics technologies can generate massive amounts of biomedical data, providing unprecedented opportunities for individualized precision medicine. However, traditional statistical methods cannot effectively process and utilize such big data. To meet this new challenge, machine learning algorithms have been developed and applied rapidly in recent years, which are capable of reducing dimensionality, extracting features, organizing data and forming automatable data-driven clinical decision systems. Data-driven clinical decision-making have promising applications in precision medicine and has been studied in digestive diseases, including early diagnosis and screening, molecular typing, staging and stratification of digestive malignancies, as well as precise diagnosis of Crohn's disease, auxiliary diagnosis of imaging and endoscopy, differential diagnosis of cystic lesions, etiology discrimination of acute abdominal pain, stratification of upper gastrointestinal bleeding (UGIB), and real-time diagnosis of esophageal motility function, showing good application prospects. Herein, we reviewed the recent progress of data-driven clinical decision making in precision diagnosis of digestive diseases and discussed the limitations of data-driven decision making after a brief introduction of methods for data-driven decision making.
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Affiliation(s)
- Song Jiang
- Department of Gastroenterology, The First Affiliated Hospital of Nanchang University, No. 17, Yongwai Zheng Street, Nanchang, 330006 China
- Jiangxi Institute of Gastroenterology and Hepatology, Nanchang, 330006 China
| | - Ting Wang
- Department of Gastroenterology, The First Affiliated Hospital of Nanchang University, No. 17, Yongwai Zheng Street, Nanchang, 330006 China
- Jiangxi Institute of Gastroenterology and Hepatology, Nanchang, 330006 China
| | - Kun-He Zhang
- Department of Gastroenterology, The First Affiliated Hospital of Nanchang University, No. 17, Yongwai Zheng Street, Nanchang, 330006 China
- Jiangxi Institute of Gastroenterology and Hepatology, Nanchang, 330006 China
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Tang S, Mao S, Chen Y, Tan F, Duan L, Pian C, Zeng X. LRBmat: A novel gut microbial interaction and individual heterogeneity inference method for colorectal cancer. J Theor Biol 2023; 571:111538. [PMID: 37257720 DOI: 10.1016/j.jtbi.2023.111538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 05/07/2023] [Accepted: 05/18/2023] [Indexed: 06/02/2023]
Abstract
The gut microbial community has been shown to play a significant role in various diseases, including colorectal cancer (CRC), which is a major public health concern worldwide. The accurate diagnosis and etiological analysis of CRC are crucial issues. Numerous methods have utilized gut microbiota to address these challenges; however, few have considered the complex interactions and individual heterogeneity of the gut microbiota, which are important issues in genetics and intestinal microbiology, particularly in high-dimensional cases. This paper presents a novel method called Binary matrix based on Logistic Regression (LRBmat) to address these concerns. The binary matrix in LRBmat can directly mitigate or eliminate the influence of heterogeneity, while also capturing information on gut microbial interactions with any order. LRBmat is highly adaptable and can be combined with any machine learning method to enhance its capabilities. The proposed method was evaluated using real CRC data and demonstrated superior classification performance compared to state-of-the-art methods. Furthermore, the association rules extracted from the binary matrix of the real data align well with biological properties and existing literature, thereby aiding in the etiological analysis of CRC.
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Affiliation(s)
- Shan Tang
- Department of Statistics, Hunan University, Changsha 410006, China
| | - Shanjun Mao
- Department of Statistics, Hunan University, Changsha 410006, China.
| | - Yangyang Chen
- Department of Computer Science, University of Tsukuba, Tsukuba 3058577, Japan
| | - Falong Tan
- Department of Statistics, Hunan University, Changsha 410006, China
| | - Lihua Duan
- Department of Rheumatology and Clinical Immunology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang University, Nanchang 330006, China
| | - Cong Pian
- College of Sciences, Nanjing Agricultural University, Nanjing 210095, China
| | - Xiangxiang Zeng
- Department of Computer Science, Hunan University, Changsha 410086, China
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Venugopal G, Khan ZH, Dash R, Tulsian V, Agrawal S, Rout S, Mahajan P, Ramadass B. Predictive association of gut microbiome and NLR in anemic low middle-income population of Odisha- a cross-sectional study. Front Nutr 2023; 10:1200688. [PMID: 37528994 PMCID: PMC10390256 DOI: 10.3389/fnut.2023.1200688] [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: 04/05/2023] [Accepted: 06/27/2023] [Indexed: 08/03/2023] Open
Abstract
Background Iron is abundant on earth but not readily available for colonizing bacteria due to its low solubility in the human body. Hosts and microbiota compete fiercely for iron. <15% Supplemented Iron is absorbed in the small bowel, and the remaining iron is a source of dysbiosis. The gut microbiome signatures to the level of predicting anemia among low-middle-income populations are unknown. The present study was conducted to identify gut microbiome signatures that have predictive potential in association with Neutrophil to lymphocytes ratio (NLR) and Mean corpuscular volume (MCV) in anemia. Methods One hundred and four participants between 10 and 70 years were recruited from Odisha's Low Middle-Income (LMI) rural population. Hematological parameters such as Hemoglobin (HGB), NLR, and MCV were measured, and NLR was categorized using percentiles. The microbiome signatures were analyzed from 61 anemic and 43 non-anemic participants using 16 s rRNA sequencing, followed by the Bioinformatics analysis performed to identify the diversity, correlations, and indicator species. The Multi-Layered Perceptron Neural Network (MLPNN) model were applied to predict anemia. Results Significant microbiome diversity among anemic participants was observed between the lower, middle, and upper Quartile NLR groups. For anemic participants with NLR in the lower quartile, alpha indices indicated bacterial overgrowth, and consistently, we identified R. faecis and B. uniformis were predominating. Using ROC analysis, R. faecis had better distinction (AUC = 0.803) to predict anemia with lower NLR. In contrast, E. biforme and H. parainfluenzae were indicators of the NLR in the middle and upper quartile, respectively. While in Non-anemic participants with low MCV, the bacterial alteration was inversely related to gender. Furthermore, our Multi-Layered Perceptron Neural Network (MLPNN) models also provided 89% accuracy in predicting Anemic or Non-Anemic from the top 20 OTUs, HGB level, NLR, MCV, and indicator species. Conclusion These findings strongly associate anemic hematological parameters and microbiome. Such predictive association between the gut microbiome and NLR could be further evaluated and utilized to design precision nutrition models and to predict Iron supplementation and dietary intervention responses in both community and clinical settings.
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Affiliation(s)
- Giriprasad Venugopal
- Center of Excellence for Clinical Microbiome Research (CCMR), All India Institute of Medical Sciences (AIIMS), Bhubaneswar, Odisha, India
| | - Zaiba Hasan Khan
- Center of Excellence for Clinical Microbiome Research (CCMR), All India Institute of Medical Sciences (AIIMS), Bhubaneswar, Odisha, India
| | - Rishikesh Dash
- Center of Excellence for Clinical Microbiome Research (CCMR), All India Institute of Medical Sciences (AIIMS), Bhubaneswar, Odisha, India
| | - Vinay Tulsian
- Center of Excellence for Clinical Microbiome Research (CCMR), All India Institute of Medical Sciences (AIIMS), Bhubaneswar, Odisha, India
| | - Siwani Agrawal
- Department of Biochemistry, All India Institute of Medical Sciences, Bhubaneswar, Odisha, India
| | - Sudeshna Rout
- Department of Biochemistry, All India Institute of Medical Sciences, Bhubaneswar, Odisha, India
| | - Preetam Mahajan
- Department of Community Medicine and Family Medicine, All India Institute of Medical Sciences (AIIMS), Bhubaneswar, Odisha, India
| | - Balamurugan Ramadass
- Center of Excellence for Clinical Microbiome Research (CCMR), All India Institute of Medical Sciences (AIIMS), Bhubaneswar, Odisha, India
- Department of Biochemistry, All India Institute of Medical Sciences, Bhubaneswar, Odisha, India
- Adelaide Medical School Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA, Australia
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Bostanci E, Kocak E, Unal M, Guzel MS, Acici K, Asuroglu T. Machine Learning Analysis of RNA-seq Data for Diagnostic and Prognostic Prediction of Colon Cancer. SENSORS (BASEL, SWITZERLAND) 2023; 23:3080. [PMID: 36991790 PMCID: PMC10052105 DOI: 10.3390/s23063080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 03/09/2023] [Accepted: 03/11/2023] [Indexed: 06/19/2023]
Abstract
Data from omics studies have been used for prediction and classification of various diseases in biomedical and bioinformatics research. In recent years, Machine Learning (ML) algorithms have been used in many different fields related to healthcare systems, especially for disease prediction and classification tasks. Integration of molecular omics data with ML algorithms has offered a great opportunity to evaluate clinical data. RNA sequence (RNA-seq) analysis has been emerged as the gold standard for transcriptomics analysis. Currently, it is being used widely in clinical research. In our present work, RNA-seq data of extracellular vesicles (EV) from healthy and colon cancer patients are analyzed. Our aim is to develop models for prediction and classification of colon cancer stages. Five different canonical ML and Deep Learning (DL) classifiers are used to predict colon cancer of an individual with processed RNA-seq data. The classes of data are formed on the basis of both colon cancer stages and cancer presence (healthy or cancer). The canonical ML classifiers, which are k-Nearest Neighbor (kNN), Logistic Model Tree (LMT), Random Tree (RT), Random Committee (RC), and Random Forest (RF), are tested with both forms of the data. In addition, to compare the performance with canonical ML models, One-Dimensional Convolutional Neural Network (1-D CNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM) DL models are utilized. Hyper-parameter optimizations of DL models are constructed by using genetic meta-heuristic optimization algorithm (GA). The best accuracy in cancer prediction is obtained with RC, LMT, and RF canonical ML algorithms as 97.33%. However, RT and kNN show 95.33% performance. The best accuracy in cancer stage classification is achieved with RF as 97.33%. This result is followed by LMT, RC, kNN, and RT with 96.33%, 96%, 94.66%, and 94%, respectively. According to the results of the experiments with DL algorithms, the best accuracy in cancer prediction is obtained with 1-D CNN as 97.67%. BiLSTM and LSTM show 94.33% and 93.67% performance, respectively. In classification of the cancer stages, the best accuracy is achieved with BiLSTM as 98%. 1-D CNN and LSTM show 97% and 94.33% performance, respectively. The results reveal that both canonical ML and DL models may outperform each other for different numbers of features.
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Affiliation(s)
- Erkan Bostanci
- Department of Computer Engineering, Faculty of Engineering, Ankara University, 06830 Ankara, Turkey
| | - Engin Kocak
- Department of Analytical Chemistry, Faculty of Gülhane Pharmacy, University of Health Sciences, 06018 Ankara, Turkey
| | - Metehan Unal
- Department of Computer Engineering, Faculty of Engineering, Ankara University, 06830 Ankara, Turkey
| | - Mehmet Serdar Guzel
- Department of Computer Engineering, Faculty of Engineering, Ankara University, 06830 Ankara, Turkey
| | - Koray Acici
- Department of Artificial Intelligence and Data Engineering, Faculty of Engineering, Ankara University, 06830 Ankara, Turkey
| | - Tunc Asuroglu
- Faculty of Medicine and Health Technology, Tampere University, 33720 Tampere, Finland
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Loganathan T, Priya Doss C G. The influence of machine learning technologies in gut microbiome research and cancer studies - A review. Life Sci 2022; 311:121118. [DOI: 10.1016/j.lfs.2022.121118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 10/19/2022] [Accepted: 10/19/2022] [Indexed: 11/18/2022]
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Swanberg KM, Kurada AV, Prinsen H, Juchem C. Multiple sclerosis diagnosis and phenotype identification by multivariate classification of in vivo frontal cortex metabolite profiles. Sci Rep 2022; 12:13888. [PMID: 35974117 PMCID: PMC9381573 DOI: 10.1038/s41598-022-17741-8] [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: 10/03/2021] [Accepted: 07/29/2022] [Indexed: 12/04/2022] Open
Abstract
Multiple sclerosis (MS) is a heterogeneous autoimmune disease for which diagnosis continues to rely on subjective clinical judgment over a battery of tests. Proton magnetic resonance spectroscopy (1H MRS) enables the noninvasive in vivo detection of multiple small-molecule metabolites and is therefore in principle a promising means of gathering information sufficient for multiple sclerosis diagnosis and subtype classification. Here we show that supervised classification using 1H-MRS-visible normal-appearing frontal cortex small-molecule metabolites alone can indeed differentiate individuals with progressive MS from control (held-out validation sensitivity 79% and specificity 68%), as well as between relapsing and progressive MS phenotypes (held-out validation sensitivity 84% and specificity 74%). Post hoc assessment demonstrated the disproportionate contributions of glutamate and glutamine to identifying MS status and phenotype, respectively. Our finding establishes 1H MRS as a viable means of characterizing progressive multiple sclerosis disease status and paves the way for continued refinement of this method as an auxiliary or mainstay of multiple sclerosis diagnostics.
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Affiliation(s)
- Kelley M. Swanberg
- grid.25879.310000 0004 1936 8972Department of Biomedical Engineering, Columbia University Fu Foundation School of Engineering and Applied Science, 351 Engineering Terrace, 1210 Amsterdam Avenue, Mail Code: 8904, New York, NY 10027 USA ,grid.47100.320000000419368710Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT USA
| | - Abhinav V. Kurada
- grid.25879.310000 0004 1936 8972Department of Biomedical Engineering, Columbia University Fu Foundation School of Engineering and Applied Science, 351 Engineering Terrace, 1210 Amsterdam Avenue, Mail Code: 8904, New York, NY 10027 USA
| | - Hetty Prinsen
- grid.47100.320000000419368710Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT USA
| | - Christoph Juchem
- grid.25879.310000 0004 1936 8972Department of Biomedical Engineering, Columbia University Fu Foundation School of Engineering and Applied Science, 351 Engineering Terrace, 1210 Amsterdam Avenue, Mail Code: 8904, New York, NY 10027 USA ,grid.47100.320000000419368710Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT USA ,grid.21729.3f0000000419368729Department of Radiology, Columbia University College of Physicians and Surgeons, New York, NY USA ,grid.47100.320000000419368710Department of Neurology, Yale University School of Medicine, New Haven, CT USA
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The crosstalk of the human microbiome in breast and colon cancer: A metabolomics analysis. Crit Rev Oncol Hematol 2022; 176:103757. [PMID: 35809795 DOI: 10.1016/j.critrevonc.2022.103757] [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: 05/27/2022] [Revised: 06/28/2022] [Accepted: 07/04/2022] [Indexed: 11/20/2022] Open
Abstract
The human microbiome's role in colon and breast cancer is described in this review. Understanding how the human microbiome and metabolomics interact with breast and colon cancer is the chief area of this study. First, the role of the gut and distal microbiome in breast and colon cancer is investigated, and the direct relationship between microbial dysbiosis and breast and colon cancer is highlighted. This work also focuses on the many metabolomic techniques used to locate prospective biomarkers, make an accurate diagnosis, and research new therapeutic targets for cancer treatment. This review clarifies the influence of anti-tumor medications on the microbiota and the proactive measures that can be taken to treat cancer using a variety of therapies, including radiotherapy, chemotherapy, next-generation biotherapeutics, gene-based therapy, integrated omics technology, and machine learning.
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Leske M, Bottacini F, Afli H, Andrade BGN. BiGAMi: Bi-Objective Genetic Algorithm Fitness Function for Feature Selection on Microbiome Datasets. Methods Protoc 2022; 5:mps5030042. [PMID: 35645350 PMCID: PMC9149982 DOI: 10.3390/mps5030042] [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: 03/28/2022] [Revised: 05/16/2022] [Accepted: 05/18/2022] [Indexed: 11/23/2022] Open
Abstract
The relationship between the host and the microbiome, or the assemblage of microorganisms (including bacteria, archaea, fungi, and viruses), has been proven crucial for its health and disease development. The high dimensionality of microbiome datasets has often been addressed as a major difficulty for data analysis, such as the use of machine-learning (ML) and deep-learning (DL) models. Here, we present BiGAMi, a bi-objective genetic algorithm fitness function for feature selection in microbial datasets to train high-performing phenotype classifiers. The proposed fitness function allowed us to build classifiers that outperformed the baseline performance estimated by the original studies by using as few as 0.04% to 2.32% features of the original dataset. In 35 out of 42 performance comparisons between BiGAMi and other feature selection methods evaluated here (sequential forward selection, SelectKBest, and GARS), BiGAMi achieved its results by selecting 6–93% fewer features. This study showed that the application of a bi-objective GA fitness function against microbiome datasets succeeded in selecting small subsets of bacteria whose contribution to understood diseases and the host state was already experimentally proven. Applying this feature selection approach to novel diseases is expected to quickly reveal the microbes most relevant to a specific condition.
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Affiliation(s)
- Mike Leske
- Department of Computer Sciences, Munster Technological University, MTU/ADAPT, T12 P928 Cork, Ireland;
| | - Francesca Bottacini
- Department of Biological Sciences, Munster Technological University, MTU, T12 P928 Cork, Ireland;
| | - Haithem Afli
- Department of Computer Sciences, Munster Technological University, MTU/ADAPT, T12 P928 Cork, Ireland;
- Correspondence: (H.A.); (B.G.N.A.)
| | - Bruno G. N. Andrade
- Department of Computer Sciences, Munster Technological University, MTU/ADAPT, T12 P928 Cork, Ireland;
- Correspondence: (H.A.); (B.G.N.A.)
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Host phenotype classification from human microbiome data is mainly driven by the presence of microbial taxa. PLoS Comput Biol 2022; 18:e1010066. [PMID: 35446845 PMCID: PMC9064115 DOI: 10.1371/journal.pcbi.1010066] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 05/03/2022] [Accepted: 03/29/2022] [Indexed: 12/14/2022] Open
Abstract
Machine learning-based classification approaches are widely used to predict host phenotypes from microbiome data. Classifiers are typically employed by considering operational taxonomic units or relative abundance profiles as input features. Such types of data are intrinsically sparse, which opens the opportunity to make predictions from the presence/absence rather than the relative abundance of microbial taxa. This also poses the question whether it is the presence rather than the abundance of particular taxa to be relevant for discrimination purposes, an aspect that has been so far overlooked in the literature. In this paper, we aim at filling this gap by performing a meta-analysis on 4,128 publicly available metagenomes associated with multiple case-control studies. At species-level taxonomic resolution, we show that it is the presence rather than the relative abundance of specific microbial taxa to be important when building classification models. Such findings are robust to the choice of the classifier and confirmed by statistical tests applied to identifying differentially abundant/present taxa. Results are further confirmed at coarser taxonomic resolutions and validated on 4,026 additional 16S rRNA samples coming from 30 public case-control studies. The composition of the human microbiome has been linked to a large number of different diseases. In this context, classification methodologies based on machine learning approaches have represented a promising tool for diagnostic purposes from metagenomics data. The link between microbial population composition and host phenotypes has been usually performed by considering taxonomic profiles represented by relative abundances of microbial species. In this study, we show that it is more the presence rather than the relative abundance of microbial taxa to be relevant to maximize classification accuracy. This is accomplished by conducting a meta-analysis on more than 4,000 shotgun metagenomes coming from 25 case-control studies and in which original relative abundance data are degraded to presence/absence profiles. Findings are also extended to 16S rRNA data and advance the research field in building prediction models directly from human microbiome data.
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14
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Pickett BE, Connor R, Berhanu-Denka T, Bhalla S, Brover V, Chambers MJ, Chaudhary K, Cissé OH, Dillman A, Elmassry MM, Feldgarden M, Holloway E, Huang X, Klimke W, Inês Mendes C, Norred SE, Parkinson J, Sevilla S, Garcia Solache M, Surujon D, Torian U, Zalunin V, Busby B. Lessons learned in virulence factor identification and data management from a hackathon on microbial virulence. F1000Res 2022. [DOI: 10.12688/f1000research.26452.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
Virulence is a complex mix of microbial traits and host susceptibility that could ultimately lead to disease. The increased prevalence of multidrug resistant infections complicates treatment options, augmenting the need for developing robust computational methods and pipelines that enable researchers and clinicians to rapidly identify the underlying mechanism(s) of virulence in any given sample/isolate. Consequently, the National Center for Biotechnology and Information at the National Institutes of Health hosted an in-person hackathon in Bethesda, Maryland during July 2019 to assist with developing cloud-based methods to reduce reliance on local computational infrastructure. Groups of attendees were assigned tasks that are relevant to identifying relevant tools, constructing pipelines capable of identifying microbial virulence factors, and managing the associated data and metadata. Specifically, the assigned tasks consisted of the following: data indexing, metabolic functions, virulence factors, antimicrobial resistance, mobile elements in enterococci, and metatranscriptomics. The cloud-based framework established by this hackathon can be augmented and built upon by the research community to aid in the rapid identification of microbial virulence factors.
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15
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Kubinski R, Djamen-Kepaou JY, Zhanabaev T, Hernandez-Garcia A, Bauer S, Hildebrand F, Korcsmaros T, Karam S, Jantchou P, Kafi K, Martin RD. Benchmark of Data Processing Methods and Machine Learning Models for Gut Microbiome-Based Diagnosis of Inflammatory Bowel Disease. Front Genet 2022; 13:784397. [PMID: 35251123 PMCID: PMC8895431 DOI: 10.3389/fgene.2022.784397] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 01/13/2022] [Indexed: 12/14/2022] Open
Abstract
Patients with inflammatory bowel disease (IBD) wait months and undergo numerous invasive procedures between the initial appearance of symptoms and receiving a diagnosis. In order to reduce time until diagnosis and improve patient wellbeing, machine learning algorithms capable of diagnosing IBD from the gut microbiome's composition are currently being explored. To date, these models have had limited clinical application due to decreased performance when applied to a new cohort of patient samples. Various methods have been developed to analyze microbiome data which may improve the generalizability of machine learning IBD diagnostic tests. With an abundance of methods, there is a need to benchmark the performance and generalizability of various machine learning pipelines (from data processing to training a machine learning model) for microbiome-based IBD diagnostic tools. We collected fifteen 16S rRNA microbiome datasets (7,707 samples) from North America to benchmark combinations of gut microbiome features, data normalization and transformation methods, batch effect correction methods, and machine learning models. Pipeline generalizability to new cohorts of patients was evaluated with two binary classification metrics following leave-one-dataset-out cross (LODO) validation, where all samples from one study were left out of the training set and tested upon. We demonstrate that taxonomic features processed with a compositional transformation method and batch effect correction with the naive zero-centering method attain the best classification performance. In addition, machine learning models that identify non-linear decision boundaries between labels are more generalizable than those that are linearly constrained. Lastly, we illustrate the importance of generating a curated training dataset to ensure similar performance across patient demographics. These findings will help improve the generalizability of machine learning models as we move towards non-invasive diagnostic and disease management tools for patients with IBD.
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Affiliation(s)
- Ryszard Kubinski
- Phyla Technologies Inc, Montréal, QC, Canada
- *Correspondence: Ryszard Kubinski, ; Ryan D. Martin,
| | | | | | - Alex Hernandez-Garcia
- Mila, Quebec Artificial Intelligence Institute, University of Montreal, Montréal, QC, Canada
| | - Stefan Bauer
- Max Planck Institute for Intelligent Systems, Tübingen, Germany
| | - Falk Hildebrand
- Gut Microbes and Health, Quadram Institute Bioscience, Norwich, United Kingdom
- Earlham Institute, Norwich, United Kingdom
| | - Tamas Korcsmaros
- Gut Microbes and Health, Quadram Institute Bioscience, Norwich, United Kingdom
- Earlham Institute, Norwich, United Kingdom
| | - Sani Karam
- Phyla Technologies Inc, Montréal, QC, Canada
| | - Prévost Jantchou
- Centre Hospitalier Universitaire Sainte-Justine, Montréal, QC, Canada
| | - Kamran Kafi
- Phyla Technologies Inc, Montréal, QC, Canada
| | - Ryan D. Martin
- Phyla Technologies Inc, Montréal, QC, Canada
- *Correspondence: Ryszard Kubinski, ; Ryan D. Martin,
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Busa J, Polaka I. Variability of Classification Results in Data with High Dimensionality and Small Sample Size. INFORMATION TECHNOLOGY AND MANAGEMENT SCIENCE 2021. [DOI: 10.7250/itms-2021-0007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
The study focuses on the analysis of biological data containing information on the number of genome sequences of intestinal microbiome bacteria before and after antibiotic use. The data have high dimensionality (bacterial taxa) and a small number of records, which is typical of bioinformatics data. Classification models induced on data sets like this usually are not stable and the accuracy metrics have high variance. The aim of the study is to create a preprocessing workflow and a classification model that can perform the most accurate classification of the microbiome into groups before and after the use of antibiotics and lessen the variability of accuracy measures of the classifier. To evaluate the accuracy of the model, measures of the area under the ROC curve and the overall accuracy of the classifier were used. In the experiments, the authors examined how classification results were affected by feature selection and increased size of the data set.
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Affiliation(s)
- Jana Busa
- Riga Technical University, Riga, Latvia
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17
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Urushiyama D, Ohnishi E, Suda W, Kurakazu M, Kiyoshima C, Hirakawa T, Miyata K, Yotsumoto F, Nabeshima K, Setoue T, Nagamitsu S, Hattori M, Hata K, Miyamoto S. Vaginal microbiome as a tool for prediction of chorioamnionitis in preterm labor: a pilot study. Sci Rep 2021; 11:18971. [PMID: 34556804 PMCID: PMC8460623 DOI: 10.1038/s41598-021-98587-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 09/08/2021] [Indexed: 11/17/2022] Open
Abstract
Intra-amniotic infection (IAI) is a major cause of preterm birth with a poor perinatal prognosis. We aimed to determine whether analyzing vaginal microbiota can evaluate the risk of chorioamnionitis (CAM) in preterm labor cases. Vaginal discharge samples were collected from 83 pregnant women admitted for preterm labor. Based on Blanc's classification, the participants were divided into CAM (stage ≥ II; n = 46) and non-CAM (stage ≤ I; n = 37) groups. The 16S rDNA amplicons (V1-V2) from vaginal samples were sequenced and analyzed. Using a random forest algorithm, the bacterial species associated with CAM were identified, and a predictive CAM (PCAM) scoring method was developed. The α diversity was significantly higher in the CAM than in the non-CAM group (P < 0.001). The area under the curve was 0.849 (95% confidence interval 0.765-0.934) using the PCAM score. Among patients at < 35 weeks of gestation, the PCAM group (n = 22) had a significantly shorter extended gestational period than the non-PCAM group (n = 25; P = 0.022). Multivariate analysis revealed a significant difference in the frequency of developmental disorders in 3-year-old infants (PCAM, 28%, non-PCAM, 4%; P = 0.022). Analyzing vaginal microbiota can evaluate the risk of IAI. Future studies should establish appropriate interventions for IAI high-risk patients to improve perinatal prognosis.
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Affiliation(s)
- Daichi Urushiyama
- Department of Obstetrics and Gynecology, Faculty of Medicine, Fukuoka University, 7-45-1 Nanakuma, Jonan-ku, Fukuoka, 814-0180, Japan
| | - Eriko Ohnishi
- Department of Maternal-Fetal Biology, National Research Institute for Child Health and Development, Tokyo, 157-8535, Japan
| | - Wataru Suda
- Laboratory for Microbiome Sciences, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan
| | - Masamitsu Kurakazu
- Department of Obstetrics and Gynecology, Faculty of Medicine, Fukuoka University, 7-45-1 Nanakuma, Jonan-ku, Fukuoka, 814-0180, Japan
| | - Chihiro Kiyoshima
- Department of Obstetrics and Gynecology, Faculty of Medicine, Fukuoka University, 7-45-1 Nanakuma, Jonan-ku, Fukuoka, 814-0180, Japan
| | - Toyofumi Hirakawa
- Department of Obstetrics and Gynecology, Faculty of Medicine, Fukuoka University, 7-45-1 Nanakuma, Jonan-ku, Fukuoka, 814-0180, Japan
| | - Kohei Miyata
- Department of Obstetrics and Gynecology, Faculty of Medicine, Fukuoka University, 7-45-1 Nanakuma, Jonan-ku, Fukuoka, 814-0180, Japan
| | - Fusanori Yotsumoto
- Department of Obstetrics and Gynecology, Faculty of Medicine, Fukuoka University, 7-45-1 Nanakuma, Jonan-ku, Fukuoka, 814-0180, Japan
| | - Kazuki Nabeshima
- Department of Pathology, Fukuoka University School of Medicine and Hospital, Fukuoka, 814-0180, Japan
| | - Takashi Setoue
- Center for Maternal, Fetal and Neonatal Medicine, Fukuoka University Hospital, Fukuoka, 814-0180, Japan
| | - Shinichiro Nagamitsu
- Department of Pediatrics, School of Medicine, Fukuoka University, Fukuoka, 814-0180, Japan
| | - Masahira Hattori
- Laboratory for Microbiome Sciences, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan
- Graduate School of Advanced Science and Engineering, Waseda University, Tokyo, 169-8555, Japan
| | - Kenichiro Hata
- Department of Maternal-Fetal Biology, National Research Institute for Child Health and Development, Tokyo, 157-8535, Japan
| | - Shingo Miyamoto
- Department of Obstetrics and Gynecology, Faculty of Medicine, Fukuoka University, 7-45-1 Nanakuma, Jonan-ku, Fukuoka, 814-0180, Japan.
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18
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Wirbel J, Zych K, Essex M, Karcher N, Kartal E, Salazar G, Bork P, Sunagawa S, Zeller G. Microbiome meta-analysis and cross-disease comparison enabled by the SIAMCAT machine learning toolbox. Genome Biol 2021; 22:93. [PMID: 33785070 PMCID: PMC8008609 DOI: 10.1186/s13059-021-02306-1] [Citation(s) in RCA: 96] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Accepted: 02/24/2021] [Indexed: 02/08/2023] Open
Abstract
The human microbiome is increasingly mined for diagnostic and therapeutic biomarkers using machine learning (ML). However, metagenomics-specific software is scarce, and overoptimistic evaluation and limited cross-study generalization are prevailing issues. To address these, we developed SIAMCAT, a versatile R toolbox for ML-based comparative metagenomics. We demonstrate its capabilities in a meta-analysis of fecal metagenomic studies (10,803 samples). When naively transferred across studies, ML models lost accuracy and disease specificity, which could however be resolved by a novel training set augmentation strategy. This reveals some biomarkers to be disease-specific, with others shared across multiple conditions. SIAMCAT is freely available from siamcat.embl.de .
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Affiliation(s)
- Jakob Wirbel
- Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), 69117 Heidelberg, Germany
| | - Konrad Zych
- Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), 69117 Heidelberg, Germany
- Present Address: Clinical Microbiomics A/S, Ole Maaløes Vej 3, 2200 København, Denmark
| | - Morgan Essex
- Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), 69117 Heidelberg, Germany
- Present Address: Experimental and Clinical Research Center (ECRC) of the Max Delbrück Center for Molecular Medicine and Charité University Hospital, 13125 Berlin, Germany
| | - Nicolai Karcher
- Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), 69117 Heidelberg, Germany
- Department CIBIO, University of Trento, 38123 Trento, Italy
| | - Ece Kartal
- Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), 69117 Heidelberg, Germany
| | - Guillem Salazar
- Department of Biology, Institute of Microbiology and Swiss Institute of Bioinformatics, ETH Zürich, 8093 Zürich, Switzerland
| | - Peer Bork
- Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), 69117 Heidelberg, Germany
- Molecular Medicine Partnership Unit, Heidelberg, Germany
- Max Delbrück Centre for Molecular Medicine, 13125 Berlin, Germany
- Department of Bioinformatics, Biocenter, University of Würzburg, 97074 Würzburg, Germany
| | - Shinichi Sunagawa
- Department of Biology, Institute of Microbiology and Swiss Institute of Bioinformatics, ETH Zürich, 8093 Zürich, Switzerland
| | - Georg Zeller
- Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), 69117 Heidelberg, Germany
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19
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Bridges JM, Mellins ED, Cron RQ. Recent progress in the treatment of non-systemic juvenile idiopathic arthritis. Fac Rev 2021; 10:23. [PMID: 33718940 PMCID: PMC7946384 DOI: 10.12703/r/10-23] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023] Open
Abstract
Juvenile idiopathic arthritis (JIA) is a chronic inflammatory disease affecting the joints and other organs that occurs in 1 in 1,000 children in the United States. Given the various categories of JIA, interpretation of the literature can be difficult. In this review, new developments in understanding non-systemic JIA and its treatment will be covered. Recent advances in the journey toward personalized treatment in JIA will be highlighted, including a review of currently available biologic modifiers. Uveitis and the temporomandibular joint will be discussed as particularly challenging treatment issues. Recent guideline updates and literature-guided treatment decisions will be reviewed.
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Affiliation(s)
- John M Bridges
- Children’s of Alabama/University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Elizabeth D Mellins
- Department of Pediatrics, Program in Immunology, Stanford University, Stanford, California, USA
| | - Randy Q Cron
- Children’s of Alabama/University of Alabama at Birmingham, Birmingham, Alabama, USA
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20
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Vázquez-Marrufo M, Sarrias-Arrabal E, García-Torres M, Martín-Clemente R, Izquierdo G. A systematic review of the application of machine-learning algorithms in multiple sclerosis. Neurologia 2021; 38:S0213-4853(20)30431-X. [PMID: 33549371 DOI: 10.1016/j.nrl.2020.10.017] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Revised: 08/20/2020] [Accepted: 10/11/2020] [Indexed: 11/24/2022] Open
Abstract
INTRODUCTION The applications of artificial intelligence, and in particular automatic learning or "machine learning" (ML), constitute both a challenge and a great opportunity in numerous scientific, technical, and clinical disciplines. Specific applications in the study of multiple sclerosis (MS) have been no exception, and constitute an area of increasing interest in recent years. OBJECTIVE We present a systematic review of the application of ML algorithms in MS. MATERIALS AND METHODS We used the PubMed search engine, which allows free access to the MEDLINE medical database, to identify studies including the keywords "machine learning" and "multiple sclerosis." We excluded review articles, studies written in languages other than English or Spanish, and studies that were mainly technical and did not specifically apply to MS. The final selection included 76 articles, and 38 were rejected. CONCLUSIONS After the review process, we established 4 main applications of ML in MS: 1) classifying MS subtypes; 2) distinguishing patients with MS from healthy controls and individuals with other diseases; 3) predicting progression and response to therapeutic interventions; and 4) other applications. Results found to date have shown that ML algorithms may offer great support for health professionals both in clinical settings and in research into MS.
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Affiliation(s)
- M Vázquez-Marrufo
- Departamento de Psicología Experimental, Facultad de Psicología, Universidad de Sevilla, Sevilla, España.
| | - E Sarrias-Arrabal
- Departamento de Psicología Experimental, Facultad de Psicología, Universidad de Sevilla, Sevilla, España
| | - M García-Torres
- Escuela Politécnica Superior, Universidad Pablo de Olavide, Sevilla, España
| | - R Martín-Clemente
- Departamento de Teoría de la Señal y Comunicaciones, Escuela Técnica Superior de Ingeniería, Universidad de Sevilla, Sevilla, España
| | - G Izquierdo
- Unidad de Esclerosis Múltiple, Hospital VITHAS, Sevilla, España
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21
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Abstract
AbstractThis article aims to provide a thorough overview of the use of Artificial Intelligence (AI) techniques in studying the gut microbiota and its role in the diagnosis and treatment of some important diseases. The association between microbiota and diseases, together with its clinical relevance, is still difficult to interpret. The advances in AI techniques, such as Machine Learning (ML) and Deep Learning (DL), can help clinicians in processing and interpreting these massive data sets. Two research groups have been involved in this Scoping Review, working in two different areas of Europe: Florence and Sarajevo. The papers included in the review describe the use of ML or DL methods applied to the study of human gut microbiota. In total, 1109 papers were considered in this study. After elimination, a final set of 16 articles was considered in the scoping review. Different AI techniques were applied in the reviewed papers. Some papers applied ML, while others applied DL techniques. 11 papers evaluated just different ML algorithms (ranging from one to eight algorithms applied to one dataset). The remaining five papers examined both ML and DL algorithms. The most applied ML algorithm was Random Forest and it also exhibited the best performances.
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Xu X, Zhang S, Zhu X, Guo J. Superconductivity enhancement in FeSe/SrTiO 3: a review from the perspective of electron-phonon coupling. JOURNAL OF PHYSICS. CONDENSED MATTER : AN INSTITUTE OF PHYSICS JOURNAL 2020; 32:343003. [PMID: 32241002 DOI: 10.1088/1361-648x/ab85f0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2019] [Accepted: 04/02/2020] [Indexed: 06/11/2023]
Abstract
Single-layer FeSe films grown on SrTiO3, with the highest superconducting transition temperature (TC) among all the iron-based superconductors, serves as an ideal platform for studying the microscopic mechanisms of high-TCsuperconductivity. The significant role of interfacial coupling has been widely recognized, while the precise nature of theTCenhancement remains open. In this review, we focus on the investigations of the interfacial coupling in FeSe/SrTiO3from the perspective of electron-phonon coupling (EPC). The main content will include an overview of the experimental measurements associated with different theoretical models and arguments about the EPC. Especially, besides the discussions of EPC based on the measurements of electronic states, we will emphasize the analyses based on phonon measurements. A uniform picture about the nature of the EPC and its relation to theTCenhancement in FeSe/SrTiO3has still not achieved, which should be the key for further studies aiming to the in-depth understanding of high-TCsuperconductivity and the discovery of new superconductors with even enhancedTC.
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Affiliation(s)
- Xiaofeng Xu
- Beijing National Laboratory for Condensed Matter Physics and Institute of Physics, Chinese Academy of Sciences, Beijing 100190, People's Republic of China
- School of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - Shuyuan Zhang
- Laboratory of Atomic and Solid State Physics, Department of Physics, Cornell University, Ithaca, New York 14853, United States of America
| | - Xuetao Zhu
- Beijing National Laboratory for Condensed Matter Physics and Institute of Physics, Chinese Academy of Sciences, Beijing 100190, People's Republic of China
- School of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
- Songshan Lake Materials Laboratory, Dongguan, Guangdong 523808, People's Republic of China
| | - Jiandong Guo
- Beijing National Laboratory for Condensed Matter Physics and Institute of Physics, Chinese Academy of Sciences, Beijing 100190, People's Republic of China
- School of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
- Songshan Lake Materials Laboratory, Dongguan, Guangdong 523808, People's Republic of China
- Collaborative Innovation Center of Quantum Matter, Beijing 100871, People's Republic of China
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23
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Identification of Potential Oral Microbial Biomarkers for the Diagnosis of Periodontitis. J Clin Med 2020; 9:jcm9051549. [PMID: 32443919 PMCID: PMC7290295 DOI: 10.3390/jcm9051549] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 05/15/2020] [Accepted: 05/18/2020] [Indexed: 02/06/2023] Open
Abstract
Periodontitis is a chronic and multifactorial inflammatory disease that can lead to tooth loss. At present, the diagnosis for periodontitis is primarily based on clinical examination and radiographic parameters. Detecting the periodontal pathogens at the subgingival plaque requires skilled professionals to collect samples. Periodontal pathogens are also detected on various mucous membranes in patients with periodontitis. In this study, we characterized the oral microbiome profiles from buccal mucosa and supragingival space in a total of 272 healthy subjects as a control group, and periodontitis patients as a disease group. We identified 13 phyla, 193 genera, and 527 species and determined periodontitis-associated taxa. Porphyromonas gingivalis, Tannerella forsythia, Treponema denticolar, Filifactor alocis, Porphyromonas endodontalis, Fretibacterium fastiosum and Peptostreptococcus species were significantly increased in both the buccal mucosa and the supragingival space in periodontitis patients. The identified eight periodontitis-associated bacterial species were clinically validated in an independent cohort. We generated the prediction model based on the oral microbiome profiles using five machine learning algorithms, and validated its capability in predicting the status of patients with periodontitis. The results showed that the oral microbiome profiles from buccal mucosa and supragingival space can represent the microbial composition of subgingival plaque and further be utilized to identify potential microbial biomarkers for the diagnosis of periodontitis. Besides, bacterial community interaction network analysis found distinct patterns associated with dysbiosis in periodontitis. In summary, we have identified oral bacterial species from buccal and supragingival sites which can predict subgingival bacterial composition and can be used for early diagnosis of periodontitis. Therefore, our study provides an important basis for developing easy and noninvasive methods to diagnose and monitor periodontitis.
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24
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Prifti E, Chevaleyre Y, Hanczar B, Belda E, Danchin A, Clément K, Zucker JD. Interpretable and accurate prediction models for metagenomics data. Gigascience 2020; 9:giaa010. [PMID: 32150601 PMCID: PMC7062144 DOI: 10.1093/gigascience/giaa010] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2019] [Revised: 09/12/2019] [Accepted: 01/27/2020] [Indexed: 01/28/2023] Open
Abstract
BACKGROUND Microbiome biomarker discovery for patient diagnosis, prognosis, and risk evaluation is attracting broad interest. Selected groups of microbial features provide signatures that characterize host disease states such as cancer or cardio-metabolic diseases. Yet, the current predictive models stemming from machine learning still behave as black boxes and seldom generalize well. Their interpretation is challenging for physicians and biologists, which makes them difficult to trust and use routinely in the physician-patient decision-making process. Novel methods that provide interpretability and biological insight are needed. Here, we introduce "predomics", an original machine learning approach inspired by microbial ecosystem interactions that is tailored for metagenomics data. It discovers accurate predictive signatures and provides unprecedented interpretability. The decision provided by the predictive model is based on a simple, yet powerful score computed by adding, subtracting, or dividing cumulative abundance of microbiome measurements. RESULTS Tested on >100 datasets, we demonstrate that predomics models are simple and highly interpretable. Even with such simplicity, they are at least as accurate as state-of-the-art methods. The family of best models, discovered during the learning process, offers the ability to distil biological information and to decipher the predictability signatures of the studied condition. In a proof-of-concept experiment, we successfully predicted body corpulence and metabolic improvement after bariatric surgery using pre-surgery microbiome data. CONCLUSIONS Predomics is a new algorithm that helps in providing reliable and trustworthy diagnostic decisions in the microbiome field. Predomics is in accord with societal and legal requirements that plead for an explainable artificial intelligence approach in the medical field.
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Affiliation(s)
- Edi Prifti
- IRD, Sorbonne University, UMMISCO, 32 Avenue Henri Varagnat, F-93143 Bondy, France
- Institute of Cardiometabolism and Nutrition, ICAN, Integromics, 91 Boulevard de l'Hopital, F-75013, Paris, France
| | - Yann Chevaleyre
- Paris-Dauphine University, PSL Research University, CNRS, UMR 7243, LAMSADE, place du Mal. de Lattre de Tassigny, F-75016, Paris, France
| | - Blaise Hanczar
- IBISC, University Paris-Saclay, University Evry, Evry, 23 Boulevard de France, F-91034, France
| | - Eugeni Belda
- Institute of Cardiometabolism and Nutrition, ICAN, Integromics, 91 Boulevard de l'Hopital, F-75013, Paris, France
| | - Antoine Danchin
- Institut Cochin INSERM U1016−CNRS UMR8104−Université Paris Descartes, 24 Rue du Faubourg Saint-Jacques, F-75014, Paris, France
| | - Karine Clément
- Sorbonne University, INSERM, Nutrition and Obesities; Systemic Approach Research Unit (NutriOmics), 91 Boulevard de l'Hopital, F-75013, Paris, France
- Assistance Publique-Hôpitaux de Paris, Nutrition Department, CRNH Ile de France, Pitié-Salpêtrière Hospital, 91 Boulevard de l'Hopital, F-75013, Paris, France
| | - Jean-Daniel Zucker
- IRD, Sorbonne University, UMMISCO, 32 Avenue Henri Varagnat, F-93143 Bondy, France
- Institute of Cardiometabolism and Nutrition, ICAN, Integromics, 91 Boulevard de l'Hopital, F-75013, Paris, France
- Sorbonne University, INSERM, Nutrition and Obesities; Systemic Approach Research Unit (NutriOmics), 91 Boulevard de l'Hopital, F-75013, Paris, France
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