Prioritizing disease candidate genes by a gene interconnectedness-based approach

BMC Genomics

Proceedings

Yen-Hua Huang¹  Chia-Lang Hsu²  Chien-Ting Hsu²  Ueng-Cheng Yang³ 

[1] Department of Biochemistry, Faculty of Medicine, School of Medicine, National Yang-Ming University, 11221, Taipei City, Taiwan, Republic of China;Institute of Biomedical Informatics, National Yang-Ming University, 11221, Taipei City, Taiwan, Republic of China;Institute of Biomedical Informatics, National Yang-Ming University, 11221, Taipei City, Taiwan, Republic of China;Center for Systems and Synthetic Biology, National Yang-Ming University, 11221, Taipei City, Taiwan, Republic of China;

关键词: Disease Gene;    Biological Network;    Test Scenario;    Spinocerebellar Ataxia;    Respective Method;   

DOI  :  10.1186/1471-2164-12-S3-S25

来源: Springer

【 摘 要 】

BackgroundGenome-wide disease-gene finding approaches may sometimes provide us with a long list of candidate genes. Since using pure experimental approaches to verify all candidates could be expensive, a number of network-based methods have been developed to prioritize candidates. Such tools usually have a set of parameters pre-trained using available network data. This means that re-training network-based tools may be required when existing biological networks are updated or when networks from different sources are to be tried.ResultsWe developed a parameter-free method, interconnectedness (ICN), to rank candidate genes by assessing the closeness of them to known disease genes in a network. ICN was tested using 1,993 known disease-gene associations and achieved a success rate of ~44% using a protein-protein interaction network under a test scenario of simulated linkage analysis. This performance is comparable with those of other well-known methods and ICN outperforms other methods when a candidate disease gene is not directly linked to known disease genes in a network. Interestingly, we show that a combined scoring strategy could enable ICN to achieve an even better performance (~50%) than other methods used alone.ConclusionsICN, a user-friendly method, can well complement other network-based methods in the context of prioritizing candidate disease genes.

【 授权许可】

Unknown   
© Hsu et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.