代表(政治)
计算机科学
超图
机制(生物学)
人工智能
嵌入
特征学习
集合(抽象数据类型)
机器学习
补语(音乐)
疾病
理论计算机科学
生物
数学
医学
哲学
认识论
离散数学
病理
政治
政治学
法学
基因
生物化学
互补
表型
程序设计语言
作者
Dong Ouyang,Yong Liang,Jinfeng Wang,Le Li,Ning Ai,Junning Feng,Shanghui Lu,Shuilin Liao,Xiaoying Liu,Shengli Xie
标识
DOI:10.1371/journal.pcbi.1011927
摘要
Existing studies have shown that the abnormal expression of microRNAs (miRNAs) usually leads to the occurrence and development of human diseases. Identifying disease-related miRNAs contributes to studying the pathogenesis of diseases at the molecular level. As traditional biological experiments are time-consuming and expensive, computational methods have been used as an effective complement to infer the potential associations between miRNAs and diseases. However, most of the existing computational methods still face three main challenges: (i) learning of high-order relations; (ii) insufficient representation learning ability; (iii) importance learning and integration of multi-view embedding representation. To this end, we developed a H yper G raph C ontrastive L earning with view-aware A ttention M echanism and I ntegrated multi-view R epresentation (HGCLAMIR) model to discover potential miRNA-disease associations. First, hypergraph convolutional network (HGCN) was utilized to capture high-order complex relations from hypergraphs related to miRNAs and diseases. Then, we combined HGCN with contrastive learning to improve and enhance the embedded representation learning ability of HGCN. Moreover, we introduced view-aware attention mechanism to adaptively weight the embedded representations of different views, thereby obtaining the importance of multi-view latent representations. Next, we innovatively proposed integrated representation learning to integrate the embedded representation information of multiple views for obtaining more reasonable embedding information. Finally, the integrated representation information was fed into a neural network-based matrix completion method to perform miRNA-disease association prediction. Experimental results on the cross-validation set and independent test set indicated that HGCLAMIR can achieve better prediction performance than other baseline models. Furthermore, the results of case studies and enrichment analysis further demonstrated the accuracy of HGCLAMIR and unconfirmed potential associations had biological significance.
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