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SIAMCAT: user-friendly and versatile machine learning workflows for statistically rigorous microbiome analyses
[article]
2020
bioRxiv
pre-print
The human microbiome is increasingly mined for diagnostic and therapeutic biomarkers. However, computational tools tailored to such analyses are still scarce. Here, we present the SIAMCAT R package, a versatile and user-friendly toolbox for comparative metagenome analyses using machine learning (ML), statistical tests, and visualization. Based on a large meta-analysis of gut microbiome studies, we optimized the choice of ML algorithms and preprocessing routines for default workflow settings.
doi:10.1101/2020.02.06.931808
fatcat:jraubeuycrbt5chykvybdlzzuy