title: Predicting candidate genes based on combined network topological features: a case study in coronary artery disease.
abstract: Predicting candidate genes using gene expression profiles and unbiased protein-protein interactions (PPI) contributes a lot in deciphering the pathogenesis of complex diseases. Recent studies showed that there are significant disparities in network topological features between non-disease and disease genes in protein-protein interaction settings. Integrated methods could consider their characteristics comprehensively in a biological network. In this study, we introduce a novel computational method, based on combined network topological features, to construct a combined classifier and then use it to predict candidate genes for coronary artery diseases (CAD). As a result, 276 novel candidate genes were predicted and were found to share similar functions to known disease genes. The majority of the candidate genes were cross-validated by other three methods. Our method will be useful in the search for candidate genes of other diseases.
authors: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China. zhangliangcai2008@gmail.com
authors: Liangcai
authors: L
authors: Zhang
authors: Xu
authors: X
authors: Li
authors: Jingxie
authors: J
authors: Tai
authors: Wan
authors: W
authors: Li
authors: Lina
authors: L
authors: Chen
mesh_terms: D000465:Algorithms; D003324:Coronary Artery Disease / Q000235:genetics* / Q000378:metabolism; D006801:Humans; D060066:Protein Interaction Maps