Fizzy: feature subset selection for metagenomics.

ABSTRACT:

Background

Some of the current software tools for comparative metagenomics provide ecologists with the ability to investigate and explore bacterial communities using α- & β-diversity. Feature subset selection--a sub-field of machine learning--can also provide a unique insight into the differences between metagenomic or 16S phenotypes. In particular, feature subset selection methods can obtain the operational taxonomic units (OTUs), or functional features, that have a high-level of influence on the condition being studied. For example, in a previous study we have used information-theoretic feature selection to understand the differences between protein family abundances that best discriminate between age groups in the human gut microbiome.

Results

We have developed a new Pyth

SUBMITTER: Ditzler G 

PROVIDER: S-EPMC4634798 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature