Sparse distance-based learning for simultaneous multiclass classification and feature selection of metagenomic data.

ABSTRACT:

Motivation

Direct sequencing of microbes in human ecosystems (the human microbiome) has complemented single genome cultivation and sequencing to understand and explore the impact of commensal microbes on human health. As sequencing technologies improve and costs decline, the sophistication of data has outgrown available computational methods. While several existing machine learning methods have been adapted for analyzing microbiome data recently, there is not yet an efficient and dedicated algorithm available for multiclass classification of human microbiota.

Results

By combining instance-based and model-based learning, we propose a novel sparse distance-based learning method for simultaneous class prediction and feature (variable or taxa, which is used interchangeably) sele

SUBMITTER: Liu Z 

PROVIDER: S-EPMC3223360 | biostudies-literature | 2011 Dec

REPOSITORIES: biostudies-literature