title: Prediction of human disease-related gene clusters by clustering analysis.
abstract: Since genes associated with similar diseases/disorders show an increased tendency for their protein products to interact with each other through protein-protein interactions (PPI), clustering analysis obviously as an efficient technique can be easily used to predict human disease-related gene clusters/subnetworks. Firstly, we used clustering algorithms, Markov cluster algorithm (MCL), Molecular complex detection (MCODE) and Clique percolation method (CPM) to decompose human PPI network into dense clusters as the candidates of disease-related clusters, and then a log likelihood model that integrates multiple biological evidences was proposed to score these dense clusters. Finally, we identified disease-related clusters using these dense clusters if they had higher scores. The efficiency was evaluated by a leave-one-out cross validation procedure. Our method achieved a success rate with 98.59% and recovered the hidden disease-related clusters in 34.04% cases when removed one known disease gene and all its gene-disease associations. We found that the clusters decomposed by CPM outperformed MCL and MCODE as the candidates of disease-related clusters with well-supported biological significance in biological process, molecular function and cellular component of Gene Ontology (GO) and expression of human tissues. We also found that most of the disease-related clusters consisted of tissue-specific genes that were highly expressed only in one or several tissues, and a few of those were composed of housekeeping genes (maintenance genes) that were ubiquitously expressed in most of all the tissues.
authors.affiliation: School of Computer Science and Technology, Xidian University, Xi'an 710071, China. psun@mail.xidian.edu.cn
authors.forename: Peng Gang
authors.initials: PG
authors.lastname: Sun
authors.forename: Lin
authors.initials: L
authors.lastname: Gao
authors.forename: Shan
authors.initials: S
authors.lastname: Han
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D020869:Gene Expression Profiling; D030342:Genetic Diseases, Inborn / Q000235:genetics*; D020022:Genetic Predisposition to Disease; D006801:Humans; D016013:Likelihood Functions; D025941:Protein Interaction Mapping