GENNDTI: Drug-target interaction prediction using graph neural network enhanced by router nodes

doi:10.1109/JBHI.2024.3402529

	GENNDTI: Drug-target interaction prediction using graph neural network enhanced by router nodes
	Beiyuan Yang1 ; Yule Liu1 ; Junfeng Wu 2; Fang Bai3 ; Mingyue Zheng 4; Jie Zheng1
	2024
发表期刊	IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (IF:6.7[JCR-2023],7.1[5-Year])
ISSN	2168-2208
EISSN	2168-2208
卷号	PP 期号:99 页码:1-12
发表状态	已发表
DOI	10.1109/JBHI.2024.3402529
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. The proposed method is implemented in Python and the source code can be found at https://github.com/JieZheng-ShanghaiTech/GENNDTI
关键词	DTI Interpretability Graph enhancement Prior Knowledge Graph neural network
URL	查看原文
收录类别	EI
语种	英语
出版者	Institute of Electrical and Electronics Engineers Inc.
EI入藏号	20242216162198
EI主题词	Magnetic resonance imaging
EI分类号	461.1 Biomedical Engineering ; 461.6 Medicine and Pharmacology ; 461.9 Biology ; 701.2 Magnetism: Basic Concepts and Phenomena ; 723.4 Artificial Intelligence ; 746 Imaging Techniques ; 802.2 Chemical Reactions ; 921.1 Algebra ; 921.4 Combinatorial Mathematics, Includes Graph Theory, Set Theory
原始文献类型	Article in Press
来源库	IEEE
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/378324
专题	信息科学与技术学院免疫化学研究所信息科学与技术学院_硕士生信息科学与技术学院_本科生信息科学与技术学院_PI研究组_郑杰组免疫化学研究所_PI研究组_白芳组
作者单位	1.School of Information Science and Technology, ShanghaiTech University, Shanghai, China 2.Ainnocence, Shanghai, China 3.Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, ShanghaiTech University, Shanghai, China 4.Shanghai Institute of Materia Medica, Chinese Academy of Sciences, China
第一作者单位	信息科学与技术学院
第一作者的第一单位	信息科学与技术学院
推荐引用方式 GB/T 7714	Beiyuan Yang,Yule Liu,Junfeng Wu,et al. GENNDTI: Drug-target interaction prediction using graph neural network enhanced by router nodes[J]. IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS,2024,PP(99):1-12.
APA	Beiyuan Yang,Yule Liu,Junfeng Wu,Fang Bai,Mingyue Zheng,&Jie Zheng.(2024).GENNDTI: Drug-target interaction prediction using graph neural network enhanced by router nodes.IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS,PP(99),1-12.
MLA	Beiyuan Yang,et al."GENNDTI: Drug-target interaction prediction using graph neural network enhanced by router nodes".IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS PP.99(2024):1-12.