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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...
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Chapter and Conference Paper
Protein-Protein Interaction Prediction by Integrating Sequence Information and Heterogeneous Network Representation
Protein-protein interaction (PPI) plays an important role in regulating cells and signals. PPI deregulation will lead to many diseases, including pernicious anemia or cancer. Despite the ongoing efforts of the...
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Chapter and Conference Paper
Detection of Drug-Drug Interactions Through Knowledge Graph Integrating Multi-attention with Capsule Network
Drug-drug interaction (DDI) prediction is a challenging problem in drug development and disease treatment. Current computational studies mainly solve this problem by designing features and extracting features ...
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Chapter and Conference Paper
Predicting miRNA-Disease Associations via a New MeSH Headings Representation of Diseases and eXtreme Gradient Boosting
Taking into account the intrinsic high cost and time-consuming in traditional Vitro studies, a computational approach that can enable researchers to easily predict the potential miRNA-disease associations is i...
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Chapter and Conference Paper
A Unified Deep Biological Sequence Representation Learning with Pretrained Encoder-Decoder Model
Machine learning methods are increasingly being applied to model and predict biomolecular interactions, while efficient feature representation plays a vital role. To this end, a unified biological sequence dee...
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Chapter and Conference Paper
A Novel Computational Approach for Predicting Drug-Target Interactions via Network Representation Learning
Detection of drug-target interactions (DTIs) has a beneficial effect on both pathogenesis and drugs discovery. Although a huge number of DTIs have been generated recently, the number of known interactions is s...
