Inferring Drug-Related Diseases Based on Convolutional Neural Network and Gated Recurrent Unit.

ABSTRACT: Predicting novel uses for drugs using their chemical, pharmacological, and indication information contributes to minimizing costs and development periods. Most previous prediction methods focused on integrating the similarity and association information of drugs and diseases. However, they tended to construct shallow prediction models to predict drug-associated diseases, which make deeply integrating the information difficult. Further, path information between drugs and diseases is important auxiliary information for association prediction, while it is not deeply integrated. We present a deep learning-based method, CGARDP, for predicting drug-related candidate disease indications. CGARDP establishes a feature matrix by exploiting a variety of biological premises related to drugs and diseas

SUBMITTER: Xuan P 

PROVIDER: S-EPMC6696443 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature