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Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment
LJ. Marcos-Zambrano, K. Karaduzovic-Hadziabdic, T. Loncar Turukalo, P. Przymus, V. Trajkovik, O. Aasmets, M. Berland, A. Gruca, J. Hasic, K. Hron, T. Klammsteiner, M. Kolev, L. Lahti, MB. Lopes, V. Moreno, I. Naskinova, E. Org, I. Paciência, G....
Jazyk angličtina Země Švýcarsko
Typ dokumentu časopisecké články, přehledy
NLK
Directory of Open Access Journals
od 2010
Free Medical Journals
od 2010
PubMed Central
od 2010
Europe PubMed Central
od 2010
Open Access Digital Library
od 2010-01-01
Open Access Digital Library
od 2010-01-01
ROAD: Directory of Open Access Scholarly Resources
od 2010
- Publikační typ
- časopisecké články MeSH
- přehledy MeSH
The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach.
Bioinformatics Research Unit Riga Stradins University Riga Latvia
Centro de Matemática e Aplicações FCT UNL Caparica Portugal
Colorectal Cancer Group Institut de Recerca Biomedica de Bellvitge Barcelona Spain
Consortium for Biomedical Research in Epidemiology and Public Health Barcelona Spain
Department of Clinical Science University of Bergen Bergen Norway
Department of Clinical Sciences Faculty of Medicine University of Barcelona Barcelona Spain
Department of Computer Networks and Systems Silesian University of Technology Gliwice Poland
Department of Computer Science University of Crete Heraklion Greece
Department of Computing University of Turku Turku Finland
Department of Information Systems Zefat Academic College Zefat Israel
Department of Microbiology University of Innsbruck Innsbruck Austria
EPIUnit Instituto de Saúde Pública da Universidade do Porto Porto Portugal
Faculty of Computer Science and Engineering Ss Cyril and Methodius University Skopje North Macedonia
Faculty of Mathematics and Computer Science Nicolaus Copernicus University Toruń Poland
Faculty of Technical Sciences University of Novi Sad Novi Sad Serbia
Galilee Digital Health Research Center Zefat Academic College Zefat Israel
Institute of Genomics Estonian Genome Centre University of Tartu Tartu Estonia
Institute of Molecular and Cell Biology University of Tartu Tartu Estonia
NOVA Laboratory for Computer Science and Informatics FCT UNL Caparica Portugal
Oncology Data Analytics Program Catalan Institute of Oncology Barcelona Spain
School of Microbiology and APC Microbiome Ireland University College Cork Cork Ireland
South West University Neofit Rilski Blagoevgrad Bulgaria
Université Paris Saclay INRAE MGP Jouy en Josas France
University Sarajevo School of Science and Technology Sarajevo Bosnia and Herzegovina
Citace poskytuje Crossref.org
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