Finding minimum gene subsets with heuristic breadth-first search algorithm for robust tumor classification.

ABSTRACT:

Background

Previous studies on tumor classification based on gene expression profiles suggest that gene selection plays a key role in improving the classification performance. Moreover, finding important tumor-related genes with the highest accuracy is a very important task because these genes might serve as tumor biomarkers, which is of great benefit to not only tumor molecular diagnosis but also drug development.

Results

This paper proposes a novel gene selection method with rich biomedical meaning based on Heuristic Breadth-first Search Algorithm (HBSA) to find as many optimal gene subsets as possible. Due to the curse of dimensionality, this type of method could suffer from over-fitting and selection bias problems. To address these potential problems, a HBSA-based ensemb

SUBMITTER: Wang SL 

PROVIDER: S-EPMC3465202 | biostudies-literature | 2012 Jul

REPOSITORIES: biostudies-literature