DeepWalk-aware graph attention networks with CNN for circRNA–drug sensitivity association identification

计算机科学 图形 图嵌入 药物重新定位 人工智能 注意力网络 特征(语言学) 机器学习 特征学习 灵敏度(控制系统) 嵌入 计算生物学 药品 理论计算机科学 生物 工程类 药理学 哲学 语言学 电子工程
作者
Guanghui Li,Youjun Li,Cheng Liang,Jiawei Luo
出处
期刊:Briefings in Functional Genomics [Oxford University Press]
被引量:2
标识
DOI:10.1093/bfgp/elad053
摘要

Abstract Circular RNAs (circRNAs) are a class of noncoding RNA molecules that are widely found in cells. Recent studies have revealed the significant role played by circRNAs in human health and disease treatment. Several restrictions are encountered because forecasting prospective circRNAs and medication sensitivity connections through biological research is not only time-consuming and expensive but also incredibly ineffective. Consequently, the development of a novel computational method that enhances both the efficiency and accuracy of predicting the associations between circRNAs and drug sensitivities is urgently needed. Here, we present DGATCCDA, a computational method based on deep learning, for circRNA–drug sensitivity association identification. In DGATCCDA, we first construct multimodal networks from the original feature information of circRNAs and drugs. After that, we adopt DeepWalk-aware graph attention networks to sufficiently extract feature information from the multimodal networks to obtain the embedding representation of nodes. Specifically, we combine DeepWalk and graph attention network to form DeepWalk-aware graph attention networks, which can effectively capture the global and local information of graph structures. The features extracted from the multimodal networks are fused by layer attention, and eventually, the inner product approach is used to construct the association matrix of circRNAs and drugs for prediction. The ultimate experimental results obtained under 5-fold cross-validation settings show that the average area under the receiver operating characteristic curve value of DGATCCDA reaches 91.18%, which is better than those of the five current state-of-the-art calculation methods. We further guide a case study, and the excellent obtained results also show that DGATCCDA is an effective computational method for exploring latent circRNA–drug sensitivity associations.
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
热情铭完成签到 ,获得积分10
刚刚
完美麦片完成签到,获得积分10
5秒前
lll完成签到,获得积分10
6秒前
务实鞅完成签到 ,获得积分10
7秒前
量子星尘发布了新的文献求助10
12秒前
mawenyu完成签到,获得积分10
13秒前
17完成签到,获得积分20
13秒前
高大的水壶完成签到,获得积分10
14秒前
英俊的铭应助wellyou采纳,获得10
16秒前
风中的向卉完成签到 ,获得积分10
19秒前
Mp4完成签到 ,获得积分10
19秒前
凌兰完成签到 ,获得积分10
19秒前
plain完成签到,获得积分10
20秒前
陌上花开完成签到,获得积分10
21秒前
22秒前
fg2477完成签到,获得积分10
23秒前
忙碌的数学人完成签到,获得积分10
23秒前
情怀应助Engen采纳,获得10
23秒前
HJJHJH完成签到,获得积分10
25秒前
Bob发布了新的文献求助10
26秒前
27秒前
28秒前
HJJHJH发布了新的文献求助50
29秒前
JW完成签到,获得积分10
29秒前
wanci应助张参采纳,获得10
30秒前
谦让的西装完成签到 ,获得积分10
31秒前
李演员完成签到,获得积分10
32秒前
fei菲飞完成签到,获得积分10
32秒前
34秒前
Zhaowx完成签到,获得积分10
34秒前
Theprisoners完成签到,获得积分0
34秒前
木子发布了新的文献求助30
34秒前
34秒前
下课了吧完成签到,获得积分10
35秒前
丘比特应助xialuoke采纳,获得10
36秒前
zgt01发布了新的文献求助10
38秒前
linfordlu完成签到,获得积分0
38秒前
清浅发布了新的文献求助10
39秒前
风趣的涵柏完成签到,获得积分10
40秒前
42秒前
高分求助中
【提示信息,请勿应助】关于scihub 10000
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] 3000
徐淮辽南地区新元古代叠层石及生物地层 3000
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
Handbook of Industrial Diamonds.Vol2 1100
Global Eyelash Assessment scale (GEA) 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 550
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 4038235
求助须知:如何正确求助?哪些是违规求助? 3575992
关于积分的说明 11374009
捐赠科研通 3305760
什么是DOI,文献DOI怎么找? 1819276
邀请新用户注册赠送积分活动 892662
科研通“疑难数据库(出版商)”最低求助积分说明 815022