Email updates

Keep up to date with the latest news and content from BMC Genomics and BioMed Central.

This article is part of the supplement: Tenth International Conference on Bioinformatics. First ISCB Asia Joint Conference 2011 (InCoB/ISCB-Asia 2011): Computational Biology

Open Access Open Badges Proceedings

Prioritizing disease candidate genes by a gene interconnectedness-based approach

Chia-Lang Hsu1, Yen-Hua Huang2, Chien-Ting Hsu1 and Ueng-Cheng Yang13*

Author Affiliations

1 Institute of Biomedical Informatics, National Yang-Ming University, Taipei City, Taiwan 11221, Republic of China

2 Department of Biochemistry, Faculty of Medicine, School of Medicine, National Yang-Ming University, Taipei City, Taiwan 11221, Republic of China

3 Center for Systems and Synthetic Biology, National Yang-Ming University, Taipei City, Taiwan 11221, Republic of China

For all author emails, please log on.

BMC Genomics 2011, 12(Suppl 3):S25  doi:10.1186/1471-2164-12-S3-S25

Published: 30 November 2011



Genome-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.


We 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.


ICN, a user-friendly method, can well complement other network-based methods in the context of prioritizing candidate disease genes.