Identifying disease genes based on functional similarity
基于功能相似性预测疾病基因

Identifying disease genes is essential for elucidating pathogenesis and developing diagnosis and prevention mea-sures. This paper developed a computational tool, named DGP, to assess candidate genes in interested chromosome regions for their possibility relating to a given disease. DGP prioritized the candidate genes by measuring the functional similarity to the known causative genes of the disease. It evaluated the performance of DGP with a dataset containing 1045 genes related to 305 diseases. The validation results show that 56. 7% and 68. 5% of disease-associated genes are at the top 5% and top 10% of the list prioritized by DGP. Therefore, DGP can effectively help the selection of candidate genes in interested chromosome regions for mutation analysis.