Identification of Drug-Disease Associations Using a Random Walk with Restart Method and Supervised Learning.

ABSTRACT: Drug-disease correlations play an important role in revealing the mechanism of disease, finding new indications of available drugs, or drug repositioning. A variety of computational approaches were proposed to find drug-disease correlations and achieve good performances. However, these methods used a variety of network information, but integrated networks were rarely used. In addition, the role of known drug-disease association data has not been fully played. In this work, we designed a combination algorithm of random walk and supervised learning to find the drug-disease correlations. We used an integrated network to update the model and selected a gene set as the start of random walk based on the known drug-disease correlations data. The experimental results show that the proposed method

SUBMITTER: Liu X 

PROVIDER: S-EPMC9576438 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature