Engineering Commons^™

Fizzy: Feature Subset Selection For Metagenomics., Gregory Ditzler, J Calvin Morrison, Yemin Lan, Gail L Rosen

Henry M. Rowan College of Engineering Faculty Scholarship

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 …