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| Item Type: | Article |
|---|---|
| Title: | Microbiome meta-analysis and cross-disease comparison enabled by the SIAMCAT machine learning toolbox |
| Creators Name: | Wirbel, J., Zych, K., Essex, M., Karcher, N., Kartal, E., Salazar, G., Bork, P., Sunagawa, S. and Zeller, G. |
| 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. |
| Keywords: | Microbiome Data Analysis, Machine Learning, Statistical Modeling, Microbiome-Wide Association Studies (MWAS), Meta-Analysis |
| Source: | Genome Biology |
| ISSN: | 1474-760X |
| Publisher: | BioMed Central |
| Volume: | 22 |
| Number: | 1 |
| Page Range: | 93 |
| Date: | 30 March 2021 |
| Official Publication: | https://doi.org/10.1186/s13059-021-02306-1 |
| PubMed: | View item in PubMed |
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