KGRDR: a deep learning model based on knowledge graph and graph regularized integration for drug repositioning
Computational drug repositioning, serving as an effective alternative to traditional drug discovery plays a key role in optimizing drug development. This approach can accelerate the development of new therapeutic options while reducing costs and mitigating risks. In this study, we propose a novel de...
Saved in:
Main Authors: | Huimin Luo, Hui Yang, Ge Zhang, Jianlin Wang, Junwei Luo, Chaokun Yan |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2025-02-01
|
Series: | Frontiers in Pharmacology |
Subjects: | |
Online Access: | https://www.frontiersin.org/articles/10.3389/fphar.2025.1525029/full |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
DVGEDR: a drug repositioning method based on dual-view fusion and graph enhancement mechanism in heterogeneous networks
by: Dongjiang Niu, et al.
Published: (2024-12-01) -
Graph Convolution for Large-Scale Graph Node Classification Task Based on Spatial and Frequency Domain Fusion
by: Junwen Lu, et al.
Published: (2025-01-01) -
GS-DTA: integrating graph and sequence models for predicting drug-target binding affinity
by: Junwei Luo, et al.
Published: (2025-02-01) -
Drug-target binding affinity prediction based on power graph and word2vec
by: Jing Hu, et al.
Published: (2025-01-01) -
Comprehensive evaluation of pure and hybrid collaborative filtering in drug repurposing
by: Clémence Réda, et al.
Published: (2025-01-01)