Mining breast cancer genes with a network based noise-tolerant approach.

ABSTRACT:

Background

Mining novel breast cancer genes is an important task in breast cancer research. Many approaches prioritize candidate genes based on their similarity to known cancer genes, usually by integrating multiple data sources. However, different types of data often contain varying degrees of noise. For effective data integration, it's important to design methods that work robustly with respect to noise.

Results

Gene Ontology (GO) annotations were often utilized in cancer gene mining works. However, the vast majority of GO annotations were computationally derived, thus not completely accurate. A set of genes annotated with breast cancer enriched GO terms was adopted here as a set of source data with realistic noise. A novel noise tolerant approach was proposed to rank cand

SUBMITTER: Nie Y 

PROVIDER: S-EPMC3702465 | biostudies-literature | 2013 Jun

REPOSITORIES: biostudies-literature