Semantic Web Reasoning for Analyzing Gene Expression Profiles

Abstract. We argue that Semantic Web reasoning is an ideal tool for analyzing gene expression profiles and the resulting sets of differentially expressed genes produced by high-throughput microarray experiments, especially since this involves combining not only very large, but also semantically and structurally complex data and knowledge sources that are inherently distributed on the Web. In this paper, we describe an initial implementation of a full-fledged system for integrated reasoning about biological data and knowledge using Sematic Web reasoning technology and apply it to the analysis of a public pancreatic cancer dataset produced in the Pollack lab at Stanford.