Hierarchical closeness efficiently predicts disease genes in a directed signaling network |
| |
| Institution: | 1. School of Computer Engineering and Information Technology, University of Ulsan, 93 Daehak-ro, Nam-gu, Ulsan 680-749, South Korea;2. Department of Information Technology, Center for Research and Development, Hanoi University of Industry, Tu Liem, Hanoi, Viet Nam;1. Department of Biotechnology, Polytechnic University of Valencia, Spain;2. IBV-CSIC. Instituto de Biomedicina de Valencia, Consejo Superior de Investigaciones Científicas, Valencia, Spain;3. CIBERER. Centro de Investigación Biomédica en Red de Enfermedades Raras, Valencia, Spain;4. Epilepsy Unit, Neurology Dept., Hospital Vall Hebron, Barcelona, Spain;5. Department of Molecular and Cellular Biochemistry, College of Medicine, University of Kentucky, USA;6. Lafora Epilepsy Cure Initiative, USA;1. Department of Chemical Biology and Therapeutics, St. Jude Children''s Research Hospital, Memphis, TN 38105, USA;2. Center for Pharmacogenetics, Department of Pharmaceutical Sciences, University of Pittsburgh, Pittsburgh, PA 15261, USA;3. Department of Pharmaceutical Analysis and Drug Metabolism, Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China;4. Integrated Biomedical Sciences Program, University of Tennessee Health Science Center, Memphis, TN 38163, USA;5. Department of Pharmaceutical Sciences, St. Jude Children''s Research Hospital, Memphis, TN 38105, USA;1. TOTAL Refining and Chemicals, Advanced Process Control Department, Technical Direction, Le Havre, France;2. TOTAL SA Scientific Development (DG/DS), Paris, France;3. MINES ParisTech, PSL Research University, CAS – Centre automatique et systèmes, 60 bd St Michel, 75006 Paris, France |
| |
| Abstract: | BackgroundMany structural centrality measures were proposed to predict putative disease genes on biological networks. Closeness is one of the best-known structural centrality measures, and its effectiveness for disease gene prediction on undirected biological networks has been frequently reported. However, it is not clear whether closeness is effective for disease gene prediction on directed biological networks such as signaling networks.ResultsIn this paper, we first show that closeness does not significantly outperform other well-known centrality measures such as Degree, Betweenness, and PageRank for disease gene prediction on a human signaling network. In addition, we observed that prediction accuracy by the closeness measure was worse than that by a reachability measure, but closeness could efficiently predict disease genes among a set of genes with the same reachability value. Based on this observation, we devised a novel structural measure, hierarchical closeness, by combining reachability and closeness such that all genes are first ranked by the degree of reachability and then the tied genes are further ranked by closeness. We discovered that hierarchical closeness outperforms other structural centrality measures in disease gene prediction. We also found that the set of highly ranked genes in terms of hierarchical closeness is clearly different from that of hub genes with high connectivity. More interestingly, these findings were consistently reproduced in a random Boolean network model. Finally, we found that genes with relatively high hierarchical closeness are significantly likely to encode proteins in the extracellular matrix and receptor proteins in a human signaling network, supporting the fact that half of all modern medicinal drugs target receptor-encoding genes.ConclusionTaken together, hierarchical closeness proposed in this study is a novel structural measure to efficiently predict putative disease genes in a directed signaling network. |
| |
| Keywords: | Hierarchical closeness Disease gene prediction Signaling network Boolean network |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
