Predicting drug–disease associations through layer attention graph convolutional network

计算机科学 卷积(计算机科学) 疾病 药品 药物开发 人工智能 图形 药物靶点 机制(生物学) 机器学习 理论计算机科学 医学 药理学 人工神经网络 病理 认识论 哲学
作者
Zhouxin Yu,Feng Huang,Xiaohan Zhao,Wenjie Xiao,Wen Zhang
出处
期刊:Briefings in Bioinformatics [Oxford University Press]
卷期号:22 (4) 被引量:203
标识
DOI:10.1093/bib/bbaa243
摘要

Determining drug-disease associations is an integral part in the process of drug development. However, the identification of drug-disease associations through wet experiments is costly and inefficient. Hence, the development of efficient and high-accuracy computational methods for predicting drug-disease associations is of great significance.In this paper, we propose a novel computational method named as layer attention graph convolutional network (LAGCN) for the drug-disease association prediction. Specifically, LAGCN first integrates the known drug-disease associations, drug-drug similarities and disease-disease similarities into a heterogeneous network, and applies the graph convolution operation to the network to learn the embeddings of drugs and diseases. Second, LAGCN combines the embeddings from multiple graph convolution layers using an attention mechanism. Third, the unobserved drug-disease associations are scored based on the integrated embeddings. Evaluated by 5-fold cross-validations, LAGCN achieves an area under the precision-recall curve of 0.3168 and an area under the receiver-operating characteristic curve of 0.8750, which are better than the results of existing state-of-the-art prediction methods and baseline methods. The case study shows that LAGCN can discover novel associations that are not curated in our dataset.LAGCN is a useful tool for predicting drug-disease associations. This study reveals that embeddings from different convolution layers can reflect the proximities of different orders, and combining the embeddings by the attention mechanism can improve the prediction performances.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
山城小肘子完成签到,获得积分10
1秒前
陈祥发布了新的文献求助10
1秒前
小七发布了新的文献求助10
3秒前
熊囧囧发布了新的文献求助10
3秒前
666发布了新的文献求助10
3秒前
4秒前
4秒前
4秒前
冷艳的道天关注了科研通微信公众号
4秒前
5秒前
5秒前
Harbor发布了新的文献求助10
5秒前
小蘑菇应助奇奇淼采纳,获得10
5秒前
6秒前
6秒前
CipherSage应助bubu采纳,获得10
6秒前
CodeCraft应助倪小呆采纳,获得10
8秒前
Tethys发布了新的文献求助10
8秒前
txy发布了新的文献求助10
9秒前
科研达人发布了新的文献求助10
9秒前
没有昵称发布了新的文献求助10
9秒前
mysteriousue发布了新的文献求助50
10秒前
佳佳应助小七采纳,获得10
10秒前
adam发布了新的文献求助10
11秒前
陈祥完成签到,获得积分10
11秒前
NexusExplorer应助俏皮的白柏采纳,获得10
11秒前
12秒前
12秒前
深情安青应助未道采纳,获得10
12秒前
16秒前
txy完成签到,获得积分10
16秒前
16秒前
16秒前
han应助甜蜜的物语采纳,获得10
17秒前
Ava应助大方小白采纳,获得10
18秒前
路十三发布了新的文献求助10
18秒前
猪猪hero发布了新的文献求助20
19秒前
zfj发布了新的文献求助10
19秒前
湛一发布了新的文献求助10
19秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3988827
求助须知:如何正确求助?哪些是违规求助? 3531183
关于积分的说明 11252671
捐赠科研通 3269809
什么是DOI,文献DOI怎么找? 1804780
邀请新用户注册赠送积分活动 881885
科研通“疑难数据库(出版商)”最低求助积分说明 809021