Feature Extraction from Microarray Expression Data by Integration of Semantic Knowledge

Microarray techniques give biologists first peek into the molecular states of living tissues. Previous studies have proven that it is feasible to build sample classifiers using the gene expressional 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 based on the concept of virtual genes by integrating microarray expression data sets with domain knowledge embedded in gene ontology (GO) annotations. We define semantic similarity to measure the functional associations between two genes using the annotation on each GO term. We then identify the groups of genes, called virtual genes, that potentially interact with each other for a biological function. The correlation in gene expression levels of virtual genes can be used to build a sample classifier. For a colon cancer data set, the integration of microarray expression data with GO annotations significantly improves the accuracy of sample classification by more than 10%.