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.
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Yuan, F., Wang, R., Guan, M., He, G. (2010). A Novel Computational Method for Predicting Disease Genes Based on Functional Similarity. In: Huang, DS., Zhang, X., Reyes GarcÃa, C.A., Zhang, L. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2010. Lecture Notes in Computer Science(), vol 6216. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14932-0_6
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DOI: https://doi.org/10.1007/978-3-642-14932-0_6
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