Application of Latent Semantic Analysis to Clustering of Cardiovascular Gene Ontology

Cardiovascular disease (CVD) is group of diseases of the heart and blood vessels and a major global cause of death, with more people dying every year from CVDs than from any other cause. Therefore, controlling and preventing CVDs and their complex pathogenesis (i.e., influenced by genetic and lifestyle factors) has gained considerable attention. In this study, we use the LSA algorithm to cluster highly related CVD association genes. The LSA can further explore each functional gene cluster including listing of the consensus terms shared by the genes in the cluster. A comprehensive network biology analysis can integrates clustered genes in the CVD genes with pathway information by building a network of interconnected pathways. Based on LSA, the CVD association genes from the GAD database are divided into 5 clusters (k=5). The gene set enrichment analysis reveals 25 significantly pathways. Further analysis to integrate pathway analysis and to combine miRNA and Drugbank data to gain more insights in the interplay can be useful to suggest drug repositioning.