A comprehensive evaluation of multicategory classification methods for microbiomic data.

ABSTRACT:

Background

Recent advances in next-generation DNA sequencing enable rapid high-throughput quantitation of microbial community composition in human samples, opening up a new field of microbiomics. One of the promises of this field is linking abundances of microbial taxa to phenotypic and physiological states, which can inform development of new diagnostic, personalized medicine, and forensic modalities. Prior research has demonstrated the feasibility of applying machine learning methods to perform body site and subject classification with microbiomic data. However, it is currently unknown which classifiers perform best among the many available alternatives for classification with microbiomic data.

Results

In this work, we performed a systematic comparison of 18 major classifi

SUBMITTER: Statnikov A 

PROVIDER: S-EPMC3960509 | biostudies-literature | 2013 Apr

REPOSITORIES: biostudies-literature