Multiview Clustering on PPI Network for Gene Selection and Enrichment from Microarray Data

Various statistical and machine learning based algorithms have been proposed in literature for selecting an informative subset of genes from micro array data sets. The recent trend is to use functional knowledge to aid the gene selection process. In this paper we propose a clustering algorithm which generates multiple views (clusters) from the micro array expression profiles, each representing a particular facet of the data. Such multiple clusters are found to represent strongly connected regions of the known protein -- protein interaction (PPI) networks, perhaps corresponding to those responsible for certain biological processes. Thus we integrate micro array data clustering with PPI knowledge to obtain enriched gene sets. Results on benchmark micro array data sets demonstrate the competitiveness of our method compared to gene selection techniques.