Drug-Gene Interaction Prediction Method through Cross Granularity Subgraph Contrastive Learning and Attention Mechanism Fusion
[Purposes] Clarifying the interconnections between drugs and genes is an important topic in drug development. At present, the graph neural network method based on the random walk algorithm has achieved great results in identifying drug-gene interaction relationships. However, existing methods with s...
Saved in:
Main Authors: | HU Dongdong, PENG Yang, TAN Shuqiu, ZHU Xiaofei |
---|---|
Format: | Article |
Language: | English |
Published: |
Editorial Office of Journal of Taiyuan University of Technology
2025-01-01
|
Series: | Taiyuan Ligong Daxue xuebao |
Subjects: | |
Online Access: | https://tyutjournal.tyut.edu.cn/englishpaper/show-2372.html |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
HDN-DDI: a novel framework for predicting drug-drug interactions using hierarchical molecular graphs and enhanced dual-view representation learning
by: Jinchen Sun, et al.
Published: (2025-01-01) -
Accurate Spatial Heterogeneity Dissection and Gene Regulation Interpretation for Spatial Transcriptomics using Dual Graph Contrastive Learning
by: Zhuohan Yu, et al.
Published: (2025-01-01) -
Attentive Self-supervised Contrastive Learning (ASCL) for plant disease classification
by: Getinet Yilma, et al.
Published: (2025-03-01) -
Towards accurate anomaly detection for cloud system via graph-enhanced contrastive learning
by: Zhen Zhang, et al.
Published: (2024-11-01) -
Rethinking spatial-temporal contrastive learning for Urban traffic flow forecasting: multi-level augmentation framework
by: Lin Pan, et al.
Published: (2024-12-01)