Interaction-based feature selection and classification for high-dimensional biological data.

ABSTRACT:

Motivation

Epistasis or gene-gene interaction has gained increasing attention in studies of complex diseases. Its presence as an ubiquitous component of genetic architecture of common human diseases has been contemplated. However, the detection of gene-gene interaction is difficult due to combinatorial explosion.

Results

We present a novel feature selection method incorporating variable interaction. Three gene expression datasets are analyzed to illustrate our method, although it can also be applied to other types of high-dimensional data. The quality of variables selected is evaluated in two ways: first by classification error rates, then by functional relevance assessed using biological knowledge. We show that the classification error rates can be significantly reduced by

SUBMITTER: Wang H 

PROVIDER: S-EPMC3577111 | biostudies-literature | 2012 Nov

REPOSITORIES: biostudies-literature