MetaBoot: a machine learning framework of taxonomical biomarker discovery for different microbial communities based on metagenomic data
Xiaojun Wang, Xiaoquan Su, Xinping Cui, Kang Ning
TL;DR
MetaBoot is a new machine learning method for finding non-redundant taxonomical biomarkers in microbial communities using metagenomic data.
Contribution
MetaBoot combines mRMR and bootstrapping to robustly and accurately discover biomarkers for microbial communities.
Findings
MetaBoot outperforms existing methods in selecting non-redundant and discriminative biomarkers.
It is robust across datasets with varied complexity and taxonomical distribution patterns.
The method shows high accuracy and biological consistency in biomarker discovery.
Abstract
As more than 90% of species in a microbial community could not be isolated and cultivated, the metagenomic methods have become one of the most important methods to analyze microbial community as a whole. With the fast accumulation of metagenomic samples and the advance of next-generation sequencing techniques, it is now possible to qualitatively and quantitatively assess all taxa (features) in a microbial community. A set of taxa with presence/absence or their different abundances could potentially be used as taxonomical biomarkers for identification of the corresponding microbial community’s phenotype. Though there exist some bioinformatics methods for metagenomic biomarker discovery, current methods are not robust, accurate and fast enough at selection of non-redundant biomarkers for prediction of microbial community’s phenotype. In this study, we have proposed a novel method,…
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Taxonomy
TopicsAstrophysics and Cosmic Phenomena · Neutrino Physics Research · Dark Matter and Cosmic Phenomena