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Prioritizing candidate disease-related genes using computational methods and biological networks data is an important problem in bioinformatics. Random walk with restart (RWR) algorithm is widely used for this problem due to its relatively high accuracy. However, RWR is computationally expensive as it considers every node in a network. Here we propose to use a new method for prioritizing candidate genes, in which genes with low probability of association with disease genes are excluded from further consideration, thus reducing computational complexity. Experiments on real protein interaction networks show that the proposed method was computationally efficient, and more accurate than RWR, as measured by AUC scores. We applied the proposed method to prioritizing candidate genes for human diabetes type 2. The results were promising: among top 20 ranked genes, 11 are associated with diabetes, as reported in the biomedical literature.

Một phương pháp phân hạng gen gây bệnh mới dựa trên tổng xác suất liên kết trong mạng tương tác protein