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BMC Genomics. 2011 Nov 30;12 Suppl 3:S25. doi: 10.1186/1471-2164-12-S3-S25. Epub 2011 Nov 30.

Prioritizing disease candidate genes by a gene interconnectedness-based approach.

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



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.

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