Incorporating Gene Ontology in Clustering Gene Expression Data

In this paper we consider a general framework for clustering expression data that permits integration of various biological data sources through combination of corresponding dissimilarity measures. In the paper we briefly review currently published attempts to genomic data fusion and discuss a problem of validating results from clustering expression data. We apply our approach to a real microarray expression dataset which induces a correlation-based dissimilarity matrix, and use gene ontology - biological process annotations to derive GO-based dissimilarity matrix. The proposed procedure is verified using a simple knowledge-based validation measure based on protein-protein interaction database. Obtained results reveal that combining experimental data with comprehensive and reliable biological repository may improve performance of cluster analysis and yield biologically meaningful gene clusters