A systematic evaluation of high-dimensional, ensemble-based regression for exploring large model spaces in microbiome analyses.

A systematic evaluation of high-dimensional, ensemble-based regression for exploring large model spaces in microbiome analyses.

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Authors: Shankar J, Szpakowski S, Solis NV, Mounaud S, Liu H, Losada L, Nierman WC, Filler SG
Title: A systematic evaluation of high-dimensional, ensemble-based regression for exploring large model spaces in microbiome analyses.
Citation: BMC bioinformatics. 2015-02-01; 16.: 31.
Abstract:
Microbiome studies incorporate next-generation sequencing to obtain profiles of microbial communities. Data generated from these experiments are high-dimensional with a rich correlation structure but modest sample sizes. A statistical model that utilizes these microbiome profiles to explain a clinical or biological endpoint needs to tackle high-dimensionality resulting from the very large space of variable configurations. Ensemble models are a class of approaches that can address high-dimensionality by aggregating information across large model spaces. Although such models are popular in fields as diverse as economics and genetics, their performance on microbiome data has been largely unexplored.
PMID: 25638274