A weighted integration method based on graph representation learning for drug repositioning

计算机科学 药物重新定位 图形 代表(政治) 人工智能 水准点(测量) 数据挖掘 相似性(几何) 机器学习 药品 理论计算机科学 医学 精神科 地理 法学 大地测量学 图像(数学) 政治 政治学
作者
Haojie Lian,Pengju Ding,Chao Yu,Xinyu Zhang,Guozhu Liu,Bin Yu
出处
期刊:Applied Soft Computing [Elsevier BV]
卷期号:161: 111763-111763
标识
DOI:10.1016/j.asoc.2024.111763
摘要

The time-consuming and expensive nature of traditional drug discovery necessitates a cost-effective approach to facilitate disease treatment. Drug repositioning, discovering innovative applications for existing drugs, has become a viable strategy that is essential for facilitating drug discovery due to its cost-effectiveness and shorter development cycle. While existing methods assume neighbors of the target node are independent, they neglect potential neighbor interaction features. We propose a weighted integration method based on graph representation learning for drug repositioning (called WIGRL) to comprehensively consider neighborhood features and neighbor interaction features, with encoders designed for similarity networks of drugs and diseases, respectively, and a network of associations between the two. Firstly, WIGRL utilizes graph convolutional network modules to obtain the neighborhood properties of nodes in similar networks. Secondly, neighbor interaction properties in similar networks are captured by graph attention network modules. Next, projection encoders are introduced to represent the association features in the association network. Finally, a more representative, unified vector is formed by simultaneously fusing information from diverse networks. After that, the decoder receives this vector to predict associations. The findings of the experiments conducted on the Fdataset, Cdataset, and LRSSL benchmark datasets demonstrate that WIGRL outperforms the existing SOTA approaches in identifying the most real positive associations and obtains the most outstanding average metrics (AUROC of 0.9331 and AUPR of 0.5654). Notably, in the case study, WIGRL discovered new associations not recorded in the dataset, validated by clinical trials and authoritative sources. Additionally, it identified novel therapeutic candidates for two neurodegenerative diseases. The source codes and datasets are available at https://github.com/YuBinLab-QUST/WIGRL.
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
田様应助科研通管家采纳,获得10
刚刚
清爽乐菱应助科研通管家采纳,获得40
刚刚
SYLH应助科研通管家采纳,获得10
刚刚
SYLH应助科研通管家采纳,获得10
刚刚
今后应助科研通管家采纳,获得10
刚刚
NexusExplorer应助科研通管家采纳,获得10
刚刚
SYLH应助科研通管家采纳,获得10
刚刚
SYLH应助科研通管家采纳,获得10
刚刚
昏睡的蟠桃应助喜之郎采纳,获得200
刚刚
Owen应助科研通管家采纳,获得10
刚刚
刚刚
cistronic完成签到,获得积分10
刚刚
激昂的亦瑶完成签到,获得积分20
刚刚
xiaoting应助笙惗雪采纳,获得10
刚刚
星辰大海应助科研通管家采纳,获得10
1秒前
SYLH应助科研通管家采纳,获得10
1秒前
黑黑黑发布了新的文献求助10
1秒前
邓志天发布了新的文献求助10
2秒前
2秒前
梦XING发布了新的文献求助10
2秒前
orixero应助我要去看星星采纳,获得10
3秒前
梁小氓完成签到 ,获得积分10
3秒前
4秒前
情怀应助hy采纳,获得30
4秒前
4秒前
4秒前
AZE应助西西弗斯玩石头采纳,获得10
4秒前
5秒前
烟柳画桥完成签到,获得积分10
5秒前
mingyahaoa完成签到,获得积分10
5秒前
5秒前
6秒前
磊磊发布了新的文献求助30
6秒前
didi完成签到,获得积分10
7秒前
wanci应助Inory007采纳,获得10
7秒前
开心完成签到,获得积分10
9秒前
9秒前
KYRIELIU发布了新的文献求助10
9秒前
FashionBoy应助随波逐流采纳,获得10
9秒前
酷酷念瑶完成签到 ,获得积分10
10秒前
高分求助中
Picture Books with Same-sex Parented Families: Unintentional Censorship 700
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
Effective Learning and Mental Wellbeing 300
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 3974779
求助须知:如何正确求助?哪些是违规求助? 3519193
关于积分的说明 11197417
捐赠科研通 3255311
什么是DOI,文献DOI怎么找? 1797760
邀请新用户注册赠送积分活动 877150
科研通“疑难数据库(出版商)”最低求助积分说明 806187