Novel gene sets improve set-level classification of prokaryotic gene expression data.

ABSTRACT:

Background

Set-level classification of gene expression data has received significant attention recently. In this setting, high-dimensional vectors of features corresponding to genes are converted into lower-dimensional vectors of features corresponding to biologically interpretable gene sets. The dimensionality reduction brings the promise of a decreased risk of overfitting, potentially resulting in improved accuracy of the learned classifiers. However, recent empirical research has not confirmed this expectation. Here we hypothesize that the reported unfavorable classification results in the set-level framework were due to the adoption of unsuitable gene sets defined typically on the basis of the Gene ontology and the KEGG database of metabolic networks. We explore an alternative

SUBMITTER: Holec M 

PROVIDER: S-EPMC4625461 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature