Predicting Candidate Genes Based on Combined Network Topological Features: A Case Study in Coronary Artery DiseaseReport as inadecuate




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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.



Author: Liangcai Zhang , Xu Li , Jingxie Tai , Wan Li, Lina Chen

Source: http://plos.srce.hr/



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