A novel computational method for predicting disease genes based on functional similarity

Authors:
Fang Yuan;Ruichun Wang;Mingxiang Guan;Guorong He
Affiliations:
Shenzhen Institute of Information Technology, Shenzhen, China;Shenzhen Institute of Information Technology, Shenzhen, China;Shenzhen Institute of Information Technology, Shenzhen, China;Shenzhen Institute of Information Technology, Shenzhen, China
Venue:
ICIC'10 Proceedings of the Advanced intelligent computing theories and applications, and 6th international conference on Intelligent computing
Year:
2010

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Abstract

Identifying disease genes is essential for elucidating pathogenesis and developing diagnosis and prevention measures. We have developed a computational tool, named DGFinder, to assess candidate genes in interested chromosome regions for their possibility relating to a given disease. DGFinder prioritizes the candidate genes based on a new approach to measure the functional similarity to the known causative genes of the disease. The performance of DGFinder was evaluated with a dataset containing 1045 genes related to 305 diseases. The validation results showed that 16.1% and 56.7% of disease-associated genes were at the top 1 and top 5 of the list prioritized by DGFinder. Therefore, DGFinder can effectively help the selection of candidate genes in interested chromosome regions for mutation analysis.