Graph Convolutional Autoencoder and Fully-Connected Autoencoder with Attention Mechanism Based Method for Predicting Drug-Disease Associations

自编码 药品 计算机科学 图形 疾病 机制(生物学) 深度学习 节点(物理) 人工智能 机器学习 数据挖掘 理论计算机科学 医学 药理学 哲学 结构工程 认识论 病理 工程类
作者
Ping Xuan,Ling Gao,Nan Sheng,Tiangang Zhang,Toshiya Nakaguchi
出处
期刊:IEEE Journal of Biomedical and Health Informatics [Institute of Electrical and Electronics Engineers]
卷期号:25 (5): 1793-1804 被引量:49
标识
DOI:10.1109/jbhi.2020.3039502
摘要

Predicting novel uses for approved drugs helps in reducing the costs of drug development and facilitates the development process. Most of previous methods focused on the multi-source data related to drugs and diseases to predict the candidate associations between drugs and diseases. There are multiple kinds of similarities between drugs, and these similarities reflect how similar two drugs are from the different views, whereas most of the previous methods failed to deeply integrate these similarities. In addition, the topology structures of the multiple drug-disease heterogeneous networks constructed by using the different kinds of drug similarities are not fully exploited. We therefore propose GFPred, a method based on a graph convolutional autoencoder and a fully-connected autoencoder with an attention mechanism, to predict drug-related diseases. GFPred integrates drug-disease associations, disease similarities, three kinds of drug similarities and attributes of the drug nodes. Three drug-disease heterogeneous networks are constructed based on the different kinds of drug similarities. We construct a graph convolutional autoencoder module, and integrate the attributes of the drug and disease nodes in each network to learn the topology representations of each drug node and disease node. As the different kinds of drug attributes contribute differently to the prediction of drug-disease associations, we construct an attribute-level attention mechanism. A fully-connected autoencoder module is established to learn the attribute representations of the drug and disease nodes. Finally, the original features of the drug-disease node pairs are also important auxiliary information for their association prediction. A combined strategy based on a convolutional neural network is proposed to fully integrate the topology representations, the attribute representations, and the original features of the drug-disease pairs. The ablation studies showed the contributions of data related to three types of drug attributes. Comparison with other methods confirmed that GFPred achieved better performance than several state-of-the-art prediction methods. In particular, case studies confirmed that GFPred is able to retrieve more actual drug-disease associations in the top k part of the prediction results. It is helpful for biologists to discover real associations by wet-lab experiments.
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
852应助认真盼曼采纳,获得10
刚刚
1秒前
2秒前
MHX完成签到,获得积分10
3秒前
曲沛萍发布了新的文献求助30
3秒前
花影完成签到 ,获得积分10
3秒前
4秒前
4秒前
吮指鸡发布了新的文献求助10
5秒前
zhang完成签到,获得积分10
6秒前
西风惊绿完成签到,获得积分10
6秒前
哔哔应助哈皮采纳,获得10
6秒前
jiangjiang发布了新的文献求助10
7秒前
十七发布了新的文献求助10
9秒前
Hello应助Archer采纳,获得10
9秒前
三笠完成签到,获得积分10
11秒前
11秒前
Li完成签到,获得积分20
11秒前
11111完成签到 ,获得积分10
12秒前
12秒前
烟花应助爱听歌傲柔采纳,获得10
14秒前
15秒前
15秒前
零度发布了新的文献求助10
15秒前
ll发布了新的文献求助10
16秒前
Lillian发布了新的文献求助10
16秒前
17秒前
哈哈完成签到,获得积分10
17秒前
18秒前
奋斗藏花发布了新的文献求助10
18秒前
斯文败类应助长孙哲瀚采纳,获得10
18秒前
NexusExplorer应助nyfz2002采纳,获得10
20秒前
bkagyin应助科研果采纳,获得10
21秒前
21秒前
Alicyclobacillus完成签到,获得积分10
22秒前
23秒前
量子星尘发布了新的文献求助10
23秒前
24秒前
田様应助sje采纳,获得10
24秒前
24秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Cognitive Neuroscience: The Biology of the Mind 1000
Technical Brochure TB 814: LPIT applications in HV gas insulated switchgear 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Picture Books with Same-sex Parented Families: Unintentional Censorship 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
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 3969884
求助须知:如何正确求助?哪些是违规求助? 3514604
关于积分的说明 11174901
捐赠科研通 3249928
什么是DOI,文献DOI怎么找? 1795149
邀请新用户注册赠送积分活动 875599
科研通“疑难数据库(出版商)”最低求助积分说明 804891