Article
Authors: Weiwenzheng Liu, Xiaolong Zhang, Xiaoli Lin, Jing Hu
Advanced Intelligent Computing in Bioinformatics: 20th International Conference, ICIC 2024, Tianjin, China, August 5–8, 2024, Proceedings, Part II
Pages 143 - 154
Published: 05 August 2024 Publication History
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Abstract
Drug-target interaction (DTIs) prediction is crucial for drug discovery and repositioning, but traditional biological experimental methods are time-consuming and expensive. Therefore, deep learning-based methods have been widely applied in the field of DTIs prediction. In recent years, methods utilizing graph convolutional neural networks to learn the features of drug-protein pairs (DPPs) and thus achieve DTI prediction have achieved certain success. However, these methods struggle to effectively integrate the topological and semantic features of DPPs and capture their interaction relationships when learning the features of DPPs. Therefore, this paper proposes a DTI prediction model named HMFGCN-DTI, which utilizes multi-path graph convolution and graph-level attention mechanism to learn features of DPPs. Experimental results indicate that compared to other state-of-the-art methods, the proposed approach demonstrates higher accuracy and generalization capability.
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Index Terms
Drug-Target Interaction Prediction Based on Multi-path Graph Convolution and Graph-Level Attention Mechanism
Applied computing
Life and medical sciences
Bioinformatics
Computational biology
Biological networks
Molecular structural biology
Recognition of genes and regulatory elements
Computing methodologies
Machine learning
Learning paradigms
Supervised learning
Theory of computation
Theory and algorithms for application domains
Machine learning theory
Structured prediction
Index terms have been assigned to the content through auto-classification.
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Published In
Advanced Intelligent Computing in Bioinformatics: 20th International Conference, ICIC 2024, Tianjin, China, August 5–8, 2024, Proceedings, Part II
Aug 2024
504 pages
ISBN:978-981-97-5691-9
DOI:10.1007/978-981-97-5692-6
- Editors:
- De-Shuang Huang
https://ror.org/036mbz113Eastern Institute of Technology, Ningbo, China
, - Yijie Pan
https://ror.org/036mbz113Eastern Institute of Technology, Ningbo, China
, - Qinhu Zhang
https://ror.org/036mbz113Eastern Institute of Technology, Ningbo, China
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 05 August 2024
Author Tags
- Drug-target Interactions
- Deep Learning
- Drug-protein Pairs (DPP)
- Graph Convolutional Neural Networks
- Graph-level Attention Mechanism
Qualifiers
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