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Li W, Chen L, He W, Li W, Qu X, Liang B, Gao Q, Feng C, Jia X, Lv Y, Zhang S, Li X. Prioritizing disease candidate proteins in cardiomyopathy-specific protein-protein interaction networks based on "guilt by association" analysis. PLoS One 2013; 8:e71191. [PMID: 23940716 PMCID: PMC3733802 DOI: 10.1371/journal.pone.0071191] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2013] [Accepted: 06/28/2013] [Indexed: 01/12/2023] Open
Abstract
The cardiomyopathies are a group of heart muscle diseases which can be inherited (familial). Identifying potential disease-related proteins is important to understand mechanisms of cardiomyopathies. Experimental identification of cardiomyophthies is costly and labour-intensive. In contrast, bioinformatics approach has a competitive advantage over experimental method. Based on “guilt by association” analysis, we prioritized candidate proteins involving in human cardiomyopathies. We first built weighted human cardiomyopathy-specific protein-protein interaction networks for three subtypes of cardiomyopathies using the known disease proteins from Online Mendelian Inheritance in Man as seeds. We then developed a method in prioritizing disease candidate proteins to rank candidate proteins in the network based on “guilt by association” analysis. It was found that most candidate proteins with high scores shared disease-related pathways with disease seed proteins. These top ranked candidate proteins were related with the corresponding disease subtypes, and were potential disease-related proteins. Cross-validation and comparison with other methods indicated that our approach could be used for the identification of potentially novel disease proteins, which may provide insights into cardiomyopathy-related mechanisms in a more comprehensive and integrated way.
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Affiliation(s)
- Wan Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China
| | - Lina Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China
- * E-mail: (LC); (XL)
| | - Weiming He
- Institute of Opto-electronics, Harbin Institute of Technology, Harbin, Heilongjiang Province, China
| | - Weiguo Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China
| | - Xiaoli Qu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China
| | - Binhua Liang
- National Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, Manitoba, Canada
| | - Qianping Gao
- Department of Cardiology, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Chenchen Feng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China
| | - Xu Jia
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China
| | - Yana Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China
| | - Siya Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China
| | - Xia Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China
- * E-mail: (LC); (XL)
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