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Chapter and Conference Paper
A Novel Graph Representation Learning Model for Drug Repositioning Using Graph Transition Probability Matrix Over Heterogenous Information Networks
Computational drug repositioning is a promising strategy in discovering new indicators for approved or experimental drugs. However, most of computational-based methods fall short of taking into account the non...
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Chapter and Conference Paper
Multi-level Subgraph Representation Learning for Drug-Disease Association Prediction Over Heterogeneous Biological Information Network
Identifying new indications for existing drugs is a crucial role in drug research and development. Computational-based methods are normally regarded as an effective way to infer drugs with new indications. The...
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Chapter and Conference Paper
MRLDTI: A Meta-path-Based Representation Learning Model for Drug-Target Interaction Prediction
Predicting the relationships between drugs and targets is a crucial step in the course of drug discovery and development. Computational prediction of associations between drugs and targets greatly enhances the...