Drug-Disease Association Prediction Based on Meta-Path Heterogeneous Network with Global Graph Attention

被引：0
作者：
Yu, Yong [1 ,2 ]
Yang, Yujie [1 ]
Li, Xiaohan [1 ]
Gao, Yue [1 ]
Yu, Qian [1 ]
机构：
[1] School of Software, Yunnan University, Kunming
[2] Key Laboratory in Software Engineering of Yunnan Province, Kunming
来源：
Dianzi Keji Daxue Xuebao/Journal of the University of Electronic Science and Technology of China | 2024年 / 53卷 / 04期
关键词：
drug-disease association; graph attention neural network; heterogeneous graph; meta-path; prediction;
D O I：
10.12178/1001-0548.2023235
中图分类号：
O144 [集合论]; O157 [组合数学（组合学）];
学科分类号：
070104 ;
摘要：
In this paper, a heterogeneous network model based on global graph attention meta-path, named MHNGA, is proposed for drug-disease association prediction. Firstly, the data of drugs and diseases are collected, and the known drug-disease association, drug similarity and disease similarity are constructed as a heterogeneous network. Secondly, multiple meta-path-based subgraphs are introduced, and the graph attention neural network is used to extract the features of the neighbor nodes of these subgraphs, and the features are enhanced by channel attention and spatial attention mechanisms. Finally, through the evaluation of ten-fold cross-validation, MHNGA achieves 93.5% of the area under the accurate recall curve and 99.4% of the accuracy. © 2024 University of Electronic Science and Technology of China. All rights reserved.
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页码：576 / 583
页数：7
相关论文
共 38 条
  • [1] TAMIMI N A M, ELLIS P., Drug development: From concept to marketing!, Nephron Clinical Practice, 113, 3, pp. c125-c131, (2009)
  • [2] DIMASI J A, HANSEN R W, GRABOWSKI H G., The price of innovation: New estimates of drug development costs, Journal of Health Economics, 22, 2, pp. 151-185, (2003)
  • [3] DICKSON M, GAGNON J P., Key factors in the rising cost of new drug discovery and development, Nature Reviews Drug Discovery, 3, 5, pp. 417-429, (2004)
  • [4] HENRY G., Are the economics of pharmaceutical research and development changing?, PharmacoEconomics, 22, 2, pp. 15-24, (2004)
  • [5] PUSHPAKOM S, IORIO F, EYERS P A, Et al., Drug repurposing: Progress, challenges and recommendations, Nature Reviews Drug Discovery, 18, 1, pp. 41-58, (2019)
  • [6] DAI W, LIU X, GAO Y, Et al., Matrix factorization-based prediction of novel drug indications by integrating genomic space, Computational and Mathematical Methods in Medicine, (2015)
  • [7] HUANG F, QIU Y, LI Q, Et al., Predicting drug-disease associations via multi-task learning based on collective matrix factorization, Frontiers in Bioengineering and Biotechnology, 8, (2020)
  • [8] LUO H, LI M, WANG S, Et al., Computational drug repositioning using low-rank matrix approximation and randomized algorithms, Bioinformatics, 34, 11, pp. 1904-1912, (2018)
  • [9] ZENG X, ZHU S, LIU X, Et al., deepDR: A network-based deep learning approach to in silico drug repositioning, Bioinformatics, 35, 24, pp. 5191-5198, (2019)
  • [10] GOTTLIEB A, STEIN G Y, RUPPIN E, Et al., Predict: A method for inferring novel drug indications with application to personalized medicine, Molecular Systems Biology, 7, 1, (2011)
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