Clique-based data mining for related genes in a biomedical database

• Published in: BMC bioinformatics Vol. 10; no. 1; p. 205
• Main Authors: Matsunaga, Tsutomu, Yonemori, Chikara, Tomita, Etsuji, Muramatsu, Masaaki
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 01.07.2009; BioMed Central; BMC
• Subjects: Analysis; Computational Biology - methods; Data mining; Databases, Genetic; Diabetes; Gene expression; Genetic disorders; Genetics; Hypertension; Information Storage and Retrieval - methods; Insulin; Insulin resistance; Life sciences; Medical research; Methods; Online databases; Proteins; Studies; Japan
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/1471-2105-10-205

Progress in the life sciences cannot be made without integrating biomedical knowledge on numerous genes in order to help formulate hypotheses on the genetic mechanisms behind various biological phenomena, including diseases. There is thus a strong need for a way to automatically and comprehensively search from biomedical databases for related genes, such as genes in the same families and genes encoding components of the same pathways. Here we address the extraction of related genes by searching for densely-connected subgraphs, which are modeled as cliques, in a biomedical relational graph. We constructed a graph whose nodes were gene or disease pages, and edges were the hyperlink connections between those pages in the Online Mendelian Inheritance in Man (OMIM) database. We obtained over 20,000 sets of related genes (called 'gene modules') by enumerating cliques computationally. The modules included genes in the same family, genes for proteins that form a complex, and genes for components of the same signaling pathway. The results of experiments using 'metabolic syndrome'-related gene modules show that the gene modules can be used to get a coherent holistic picture helpful for interpreting relations among genes. We presented a data mining approach extracting related genes by enumerating cliques. The extracted gene sets provide a holistic picture useful for comprehending complex disease mechanisms.