Learning Multi-Types of Neighbor Node Attributes and Semantics by Heterogeneous Graph Transformer and Multi-View Attention for Drug-Related Side-Effect Prediction

Learning Multi-Types of Neighbor Node Attributes and Semantics by Heterogeneous Graph Transformer and Multi-View Attention for Drug-Related Side-Effect Prediction

Since side-effects of drugs are one of the primary reasons for their failure in clinical trials, predicting their side-effects can help reduce drug development costs. We proposed a method based on heterogeneous graph transformer and capsule networks for side-effect-drug-association prediction (TCSD)...

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Bibliographic Details
Main Authors: Ping Xuan, Peiru Li, Hui Cui, Meng Wang, Toshiya Nakaguchi, Tiangang Zhang
Format: Article
Language:English
Published: MDPI AG 2023-09-01
Series:Molecules
Subjects:
drug-related side-effect prediction
multi-types of neighbor node attributes
diverse connection semantics learning
heterogeneous graph transformer
neighbor node category-level attention
Online Access:https://www.mdpi.com/1420-3049/28/18/6544

Internet

https://www.mdpi.com/1420-3049/28/18/6544

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