Semantic similarity based feature extraction from microarray expression data. (24th June 2009)

• Record Type: Journal Article
• Authors: Cho, Young-Rae; Zhang, Aidong; Xu, Xian
• Is Part Of: International journal of data mining and bioinformatics. Volume 3:Number 3(2009)
• Page Start: 333
• Page End: 345
• Publication Date: 2009-06-24
• Subjects: feature extraction -- microarray expression data -- semantic similarity -- bioinformatics -- gene ontology -- virtual genes -- colon cancer data -- sample classifiers -- gene expression profiles -- classification accuracy; Data mining -- Periodicals; Bioinformatics -- Periodicals; 006.312
• Languages: English
• ISSNs: 1748-5673

Abstract

Previous studies have proven that it is feasible to build sample classifiers using gene expression profiles. To build an effective sample classifier, dimension reduction process is necessary since classic pattern recognition algorithms do not work well in high dimensional space. In this paper, we present a novel feature extraction algorithm by integrating microarray expression data with Gene Ontology (GO). Applying semantic similarity measures, we identify the groups of genes, called virtual genes, which potentially interact with each other for a biological function. The correlation in expressions of virtual genes is used to classify samples. For colon cancer data, this approach significantly improved the classification accuracy by more than 10%.