HGCLAMIR: Hypergraph contrastive learning with attention mechanism and integrated multi-view representation for predicting miRNA-disease associations

Abstract

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 HyperGraph Contrastive Learning with view-aware Attention Mechanism and Integrated multi-view Representation (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.

EMCMDA: predicting miRNA-disease associations via efficient matrix completion

“…In this study, the predictive capability of EMCMDA is assessed through Global LOOCV and 5-fold CV using the benchmark dataset. To assess the proposed model, we compared its predictions with those generated by HGCLAMIR 16 , BNNRMDA 24 , WBNPMD 19 , KATZBNRA 18 , PMFMDA 25 , IMCMDA 26 .

Figure 2 Global LOOCV and 5-fold CV were employed on the benchmark dataset to compare the predictive capabilities of various models.

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“…In the last few years, more and more computational models have been developed. HGCLAMIR 16 combines view-aware attention mechanisms of hypergraph contrast learning and combined multi-view representation techniques to forecast MDAs. Its advantage lies in proposing a multi-view representation integration approach, enriching embedded representation information.…”

“…In recent years, machine learning has gained widespread acceptance and produced impressive outcomes in the domain of MDA prediction. Ouyang et al 16 introduced a HGCLAMIR model. They combine integrated multi-view representation and hypergraph contrast learning techniques with view-aware attention mechanisms to forecast MDAs.…”