Microbiome meta-analysis and cross-disease comparison enabled by the SIAMCAT machine learning toolbox

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 v...

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Vydáno v:	Genome Biology Ročník 22; číslo 1; s. 93
Hlavní autoři:	Wirbel, Jakob, Zych, Konrad, Essex, Morgan, Karcher, Nicolai, Kartal, Ece, Salazar, Guillem, Bork, Peer, Sunagawa, Shinichi, Zeller, Georg
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London BioMed Central 30.03.2021 Springer Nature B.V BMC
Témata:	Algorithms Animal Genetics and Genomics Bioinformatics Biomarkers Biomedical and Life Sciences Computational Biology - methods computer software Confounding Factors, Epidemiologic Crohn Disease - etiology Databases, Genetic Datasets Evolutionary Biology Gastrointestinal Microbiome genome Human Genetics Humans Inflammatory bowel disease Learning algorithms Life Sciences Machine Learning Meta-analysis Meta-Analysis as Topic Metagenome Metagenomics Metagenomics - methods Microbial Genetics and Genomics microbiome Microbiome data analysis Microbiome-wide association studies (MWAS) Microbiomes Microbiota Models, Statistical Plant Genetics and Genomics ROC Curve Software Statistical analysis Statistical modeling Taxonomy therapeutics Variables Workflow Statistical modeling Microbiome data analysis Microbiome-wide association studies (MWAS) Machine learning Meta-analysis
ISSN:	1474-760X, 1474-7596, 1474-760X
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