Predicting lncRNA-disease associations using multiple metapaths in hierarchical graph attention networks

计算机科学 异构网络 同种类的 图形 生物网络 复杂网络 疾病 数据挖掘 机器学习 计算生物学 理论计算机科学 生物 数学 无线网络 医学 病理 电信 组合数学 万维网 无线
作者
Dengju Yao,Yun Deng,Xu Zhan,Xiaoming Zhan
出处
期刊:BMC Bioinformatics [BioMed Central]
卷期号:25 (1)
标识
DOI:10.1186/s12859-024-05672-2
摘要

Many biological studies have shown that lncRNAs regulate the expression of epigenetically related genes. The study of lncRNAs has helped to deepen our understanding of the pathogenesis of complex diseases at the molecular level. Due to the large number of lncRNAs and the complex and time-consuming nature of biological experiments, applying computer techniques to predict potential lncRNA-disease associations is very effective. To explore information between complex network structures, existing methods rely mainly on lncRNA and disease information. Metapaths have been applied to network models as an effective method for exploring information in heterogeneous graphs. However, existing methods are dominated by lncRNAs or disease nodes and tend to ignore the paths provided by intermediate nodes.We propose a deep learning model based on hierarchical graphical attention networks to predict unknown lncRNA-disease associations using multiple types of metapaths to extract features. We have named this model the MMHGAN. First, the model constructs a lncRNA-disease-miRNA heterogeneous graph based on known associations and two homogeneous graphs of lncRNAs and diseases. Second, for homogeneous graphs, the features of neighboring nodes are aggregated using a multihead attention mechanism. Third, for the heterogeneous graph, metapaths of different intermediate nodes are selected to construct subgraphs, and the importance of different types of metapaths is calculated and aggregated to obtain the final embedded features. Finally, the features are reconstructed using a fully connected layer to obtain the prediction results.We used a fivefold cross-validation method and obtained an average AUC value of 96.07% and an average AUPR value of 93.23%. Additionally, ablation experiments demonstrated the role of homogeneous graphs and different intermediate node path weights. In addition, we studied lung cancer, esophageal carcinoma, and breast cancer. Among the 15 lncRNAs associated with these diseases, 15, 12, and 14 lncRNAs were validated by the lncRNA Disease Database and the Lnc2Cancer Database, respectively.We compared the MMHGAN model with six existing models with better performance, and the case study demonstrated that the model was effective in predicting the correlation between potential lncRNAs and diseases.
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
2秒前
3秒前
4秒前
西南楚留香完成签到,获得积分10
7秒前
Newky发布了新的文献求助10
8秒前
10秒前
10秒前
王路飞完成签到,获得积分10
11秒前
14秒前
mmm发布了新的文献求助10
15秒前
boltos发布了新的文献求助10
17秒前
ASA完成签到,获得积分10
17秒前
abin发布了新的文献求助10
18秒前
Newky完成签到,获得积分10
18秒前
卿君完成签到,获得积分10
22秒前
22秒前
Jasper应助乔佳怡采纳,获得10
24秒前
Akim应助hwezhu采纳,获得10
24秒前
25秒前
alpv完成签到,获得积分10
25秒前
26秒前
南冥发布了新的文献求助10
29秒前
29秒前
30秒前
迷路的绿藻头完成签到 ,获得积分10
30秒前
量子星尘发布了新的文献求助10
31秒前
哈哈完成签到,获得积分10
31秒前
A阿澍发布了新的文献求助10
32秒前
abin完成签到,获得积分10
32秒前
34秒前
34秒前
34秒前
hwezhu发布了新的文献求助10
35秒前
37秒前
jyy应助科研通管家采纳,获得10
39秒前
czh应助科研通管家采纳,获得20
39秒前
烟花应助科研通管家采纳,获得10
40秒前
充电宝应助科研通管家采纳,获得10
40秒前
小马甲应助科研通管家采纳,获得10
40秒前
40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 3989263
求助须知:如何正确求助?哪些是违规求助? 3531418
关于积分的说明 11253814
捐赠科研通 3270066
什么是DOI,文献DOI怎么找? 1804884
邀请新用户注册赠送积分活动 882084
科研通“疑难数据库(出版商)”最低求助积分说明 809136