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.

A Network Study of Chinese Medicine Xuesaitong Injection to Elucidate a Complex Mode of Action with Multicompound, Multitarget, and Multipathway

“…In addition, many inflammatory response related pathways were also enriched, including complement and coagulation cascades, Fc epsilon RI signaling pathway, hematopoietic cell lineage, leukocyte transendothelial migration, NOD-like receptor signaling pathway, RIG-I-like receptor signaling pathway, and Toll-like receptor signaling pathway. It was suggested that XST modulates inflammatory responses in treating ischemia, which has been found to prevent damages of the inflammatory factors to cardiac cells [34–37]. The influencing in complement and coagulation cascades pathway indicated that XST and its constituent compound notoginsenoside R1 are functional in modulating the coagulation process in MI, which is one of the most common clinical risk factors [32–35].…”

Network Biology in Medicine and Beyond

“…Zhang et al . defined six network features, namely degree, neighbor count of disease genes, ratio of disease genes in neighbor, betweenness centrality, clustering coefficient, and mean shortest path length to disease gene, and trained a support vector machine classifier to predict candidate genes for coronary artery disease 69 .…”

Centrality and the shortest path approach on the human interactome

“…Clustering coefficient -to analyze network topology (Hao et al 2012). The shortest path approach is used to construct networks for a set of genes (Yuan et al 2017), to prioritize vertices (Zhang et al 2012), to predict functional components and molecular pathways (Nakamura et al 2012;Bromberg et al 2009), to perform network modularization (Cabusora et al 2005) and to predict protein function (Sharan et al 2007).…”