Iterative feature removal yields highly discriminative pathways.

ABSTRACT:

Background

We introduce Iterative Feature Removal (IFR) as an unbiased approach for selecting features with diagnostic capacity from large data sets. The algorithm is based on recently developed tools in machine learning that are driven by sparse feature selection goals. When applied to genomic data, our method is designed to identify genes that can provide deeper insight into complex interactions while remaining directly connected to diagnostic utility. We contrast this approach with the search for a minimal best set of discriminative genes, which can provide only an incomplete picture of the biological complexity.

Results

Microarray data sets typically contain far more features (genes) than samples. For this type of data, we demonstrate that there are many equivalently-pre

SUBMITTER: O'Hara S 

PROVIDER: S-EPMC3879090 | biostudies-literature | 2013 Nov

REPOSITORIES: biostudies-literature