Autor: |
Jakob Wirbel, Konrad Zych, Morgan Essex, Nicolai Karcher, Ece Kartal, Guillem Salazar, Peer Bork, Shinichi Sunagawa, Georg Zeller |
Jazyk: |
angličtina |
Rok vydání: |
2021 |
Předmět: |
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Zdroj: |
Genome Biology, Vol 22, Iss 1, Pp 1-27 (2021) |
Druh dokumentu: |
article |
ISSN: |
1474-760X |
DOI: |
10.1186/s13059-021-02306-1 |
Popis: |
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 . |
Databáze: |
Directory of Open Access Journals |
Externí odkaz: |
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