Contrastive self-supervised graph convolutional network for detecting the relationship among lncRNAs, miRNAs, and diseases

计算机科学 图形 生物学数据 机器学习 卷积神经网络 人工智能 利用 相似性(几何) 嵌入 理论计算机科学 数据挖掘 生物信息学 生物 计算机安全 图像(数学)
作者
Nan Sheng,Lan Huang,Yan Wang,Ling Gao,Huiyan Sun,Xuping Xie
标识
DOI:10.1109/bibm58861.2023.10385789
摘要

Inferring potential relationships among long non-coding RNAs (lncRNAs), microRNAs (miRNAs), and diseases play a crucial role in investigation of disease aetiology and pathogenesis. Due to the high cost of laboratory experiments, there is a practical requirement to develop appropriate computational methods that promise to accelerate the experimental screening process for potential lncRNA-disease associations (LDAs), miRNA-disease associations (MDAs), and lncRNA-miRNA interactions (LMIs). However, most existing methods are applied to predict LDAs, MDAs, and LMIs in specific domains, neglecting the important benefits of integrating multiple sources data and limiting the ability of transferring models to other tasks. Furthermore, with the high sparsity of LDA, MDA, and LMI data, it is difficult for many computational models to exploit enough knowledge to learn the comprehensive patterns of node embedding. In this study, inspired by the recent success of graph contrastive learning, we develop a Contrastive Self-supervised Graph convolutional network to identify potential LDAs, MDAs, and LMIs (called CSGLMD). CSGLMD combines supervised learning and self-supervised learning to fully capture node features. Specifically, CSGLMD primarily leverages the rich association and similarity relationships among lncRNA, miRNA, and disease to construct a lncRNA-miRNA-disease heterogeneous graph (LMDHG) that contains three types of biological entities. It can effectively embed multi-source biological data and assist the model extension to other prediction tasks. In addition, we consider applying a label instantiation mechanism to make the LMDHG better adapt graph neural network structures and control the strength of similarity relationships between the same biological entities. Secondly, CSGLMD implements graph convolutional network (GCN) as encoder to extract node embedding features from the LMDHG, and utilizes a multi-relational modelling decoder to predict LDAs, MDAs, or LMIs. Finally, we designed a contrastive self-supervised learning task that guides the learning of node embeddings without relying on labels, and acts as a regularize in a multi-task learning paradigm to enhance the generalization ability of the model. Extensive results on two datasets (from the old and new versions of the database, respectively) show that CSGLMD significantly outperforms 12 state-of-the-art methods (5 LDA prediction and 7 MDA prediction) in predicting disease-associated lncRNAs and miRNAs. Case studies on old and new datasets can further demonstrate the capability of CSGLMD to discover disease-related new candidate lncRNAs and miRNAs. The source data and code for the proposed model are publicly available on https://github.com/sheng-n/CSGLMD.
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
量子星尘发布了新的文献求助10
1秒前
宇宙第一帅完成签到,获得积分10
1秒前
墨菲特关注了科研通微信公众号
1秒前
1秒前
3秒前
SXYYXS完成签到 ,获得积分10
4秒前
5秒前
5秒前
ClaudiaCY发布了新的文献求助10
6秒前
candy发布了新的文献求助10
6秒前
豆芽完成签到,获得积分10
6秒前
pluto应助wml采纳,获得10
7秒前
iui飞完成签到,获得积分10
7秒前
7秒前
IyGnauH完成签到 ,获得积分10
8秒前
8秒前
10秒前
小巧的虔发布了新的文献求助10
10秒前
iui飞发布了新的文献求助10
10秒前
科研通AI2S应助jiang采纳,获得10
10秒前
小青龙完成签到,获得积分10
11秒前
ws完成签到,获得积分20
12秒前
12秒前
天天快乐应助1234采纳,获得10
13秒前
PRIPRO发布了新的文献求助10
13秒前
萧萧完成签到,获得积分10
13秒前
Akim应助小小小何77采纳,获得10
13秒前
shinysparrow应助烟波钓客采纳,获得200
14秒前
hetao286发布了新的文献求助10
14秒前
脑洞疼应助乐观的忘幽采纳,获得10
15秒前
泰裤辣完成签到,获得积分10
15秒前
勤劳太阳发布了新的文献求助10
18秒前
regene完成签到,获得积分10
18秒前
HelloKun发布了新的文献求助10
18秒前
wml完成签到 ,获得积分10
19秒前
LaTeXer应助平常囧采纳,获得50
19秒前
dingz完成签到,获得积分10
23秒前
可爱的函函应助七七采纳,获得10
23秒前
赘婿应助爱听歌初曼采纳,获得10
23秒前
25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
Toward a Combinatorial Approach for the Prediction of IgG Half-Life and Clearance 500
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
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 3969940
求助须知:如何正确求助?哪些是违规求助? 3514642
关于积分的说明 11175298
捐赠科研通 3249947
什么是DOI,文献DOI怎么找? 1795178
邀请新用户注册赠送积分活动 875617
科研通“疑难数据库(出版商)”最低求助积分说明 804891