GENNDTI: Drug-Target Interaction Prediction Using Graph Neural Network Enhanced by Router Nodes
文献类型：期刊论文
| 作者 | Yang, Beiyuan3; Liu, Yule3; Wu, Junfeng4; Bai, Fang1,5; Zheng, Mingyue6 ; Zheng, Jie2,3
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| 刊名 | IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
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| 出版日期 | 2024-12-01 |
| 卷号 | 28期号:12页码:7588-7598 |
| 关键词 | DTI interpretability graph enhancement prior knowledge graph neural network DTI interpretability graph enhancement prior knowledge graph neural network |
| ISSN号 | 2168-2194 |
| DOI | 10.1109/JBHI.2024.3402529 |
| 通讯作者 | Zheng, Jie(zhengjie@shanghaitech.edu.cn) |
| 英文摘要 | Identifying drug-target interactions (DTI) is crucial in drug discovery and repurposing, and in silico techniques for DTI predictions are becoming increasingly important for reducing time and cost. Most interaction-based DTI models rely on the guilt-by-association principle that "similar drugs can interact with similar targets". However, such methods utilize precomputed similarity matrices and cannot dynamically discover intricate correlations. Meanwhile, some methods enrich DTI networks by incorporating additional networks like DDI and PPI networks, enriching biological signals to enhance DTI prediction. While these approaches have achieved promising performance in DTI prediction, such coarse-grained association data do not explain the specific biological mechanisms underlying DTIs. In this work, we propose GENNDTI, which constructs biologically meaningful routers to represent and integrate the salient properties of drugs and targets. Similar drugs or targets connect to more same router nodes, capturing property sharing. In addition, heterogeneous encoders are designed to distinguish different types of interactions, modeling both real and constructed interactions. This strategy enriches graph topology and enhances prediction efficiency as well. We evaluate the proposed method on benchmark datasets, demonstrating comparative performance over existing methods. We specifically analyze router nodes to validate their efficacy in improving predictions and providing biological explanations. |
| WOS关键词 | TRANSFORMER |
| WOS研究方向 | Computer Science ; Mathematical & Computational Biology ; Medical Informatics |
| 语种 | 英语 |
| WOS记录号 | WOS:001373825400011 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.183/handle/2S10ELR8/315058]  |
| 专题 | 中国科学院上海药物研究所 |
| 通讯作者 | Zheng, Jie |
| 作者单位 | 1.ShanghaiTech Univ, Sch Life Sci & Technol, Shanghai 201210, Peoples R China 2.Shanghai Engn Res Ctr Intelligent Vis & Imaging, Shanghai 201210, Peoples R China 3.ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai 201210, Peoples R China 4.Ainnocence, Shanghai 200082, Peoples R China 5.ShanghaiTech Univ, Shanghai Inst Adv Immunochem Studies, Shanghai 201210, Peoples R China 6.Chinese Acad Sci, Shanghai Inst Mat Med, Shanghai 201210, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yang, Beiyuan,Liu, Yule,Wu, Junfeng,et al. GENNDTI: Drug-Target Interaction Prediction Using Graph Neural Network Enhanced by Router Nodes[J]. IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS,2024,28(12):7588-7598. |
| APA | Yang, Beiyuan,Liu, Yule,Wu, Junfeng,Bai, Fang,Zheng, Mingyue,&Zheng, Jie.(2024).GENNDTI: Drug-Target Interaction Prediction Using Graph Neural Network Enhanced by Router Nodes.IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS,28(12),7588-7598. |
| MLA | Yang, Beiyuan,et al."GENNDTI: Drug-Target Interaction Prediction Using Graph Neural Network Enhanced by Router Nodes".IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 28.12(2024):7588-7598. |
入库方式： OAI收割
来源：上海药物研究所
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