An ontology-driven clustering method for supporting gene expression analysis

Abstract

The gene ontology (GO) is an important knowledge resource for biologists and bioinformaticians. This paper explores the integration of similarity information derived from GO into clustering-based gene expression analysis. A system that integrates GO annotations, similarity patterns and expression data in yeast is assessed. In comparison with a clustering model based only on expression data correlation, the proposed framework not only produces consistent results, but also it offers alternative, potentially meaningful views of the biological problem under study. Moreover, it provides the basis for developing other automated, knowledge-driven data mining systems in this and related application areas.