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3 Result(s)

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  1. No Access

    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...

    Dong-Xu Li, Xun Deng, Bo-Wei Zhao… in Advanced Intelligent Computing Technology … (2023)

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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...

    Bo-Wei Zhao, **ao-Rui Su, Yue Yang… in Advanced Intelligent Computing Technology … (2023)

  3. No Access

    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...

    Bo-Wei Zhao, Lun Hu, Peng-Wei Hu… in Intelligent Computing Theories and Applica… (2022)