A Density-Based Clustering for Gene Expression Data Using Gene Ontology

Gene expression clustering is built on the premise that similarly expressed genes are included in the same kind of biological process. Recent research has focused on the fact that incorporation of biological knowledge such as gene ontology (GO) improves the result of clustering. This paper demonstrates a Semi-supervised Density-based Clustering (SDC) which uses GO to detect positive and negative co-regulated patterns from the noisy gene expression data. SDC improves a previous algorithm DenGeneClus (DGC) which could handle only positive co-regulation and did not include GO in the clustering process. Experimental results on four real-life data show that SDC outperforms DGC based on z-score and gene ontology enrichment analysis.