MCOAN: multimodal contrastive representation learning for cross-omics adaptive disease regulatory network prediction

Abstract

Motivation: Interactions among long noncoding RNAs, circular RNAs, microRNAs, and messenger RNAs form complex gene expression regulatory networks, which are of great significance for the diagnosis, prevention, and treatment of complex diseases. Although existing computational methods have been developed to predict interactions among certain molecular types, they are generally limited to single-modality perspectives, overlooking competitive specificity and co-target cooperativity across multi-omics molecules, and thereby limiting their ability to elucidate cross-omics regulatory mechanisms.

Results: We proposed a novel cross-omics adaptive multimodal contrastive learning framework (MCOAN) that learns multimodal regulatory mechanisms and effectively predicts disease-associated molecular regulatory networks. Specifically, we first constructed a five-layer heterogeneous graph architecture to comprehensively integrate the complex regulatory associations among multi-omics nodes. Then, we proposed an unsupervised multimodal contrastive learning strategy that maximizes mutual information across distinct regulatory views, thereby enhancing node representations by efficiently capturing local neighborhood structure and global semantic information. Meanwhile, we also proposed a cross-omics adaptive learning mechanism that captures complex competitive specificity and co-target cooperativity across distinct regulatory networks, thereby further enhancing the structural awareness in node representations. Furthermore, we evaluated multiple downstream classifiers to accurately predict multimodal molecular regulatory networks. Finally, extensive experiments show that MCOAN consistently outperforms existing methods, achieving strong predictive accuracy and generalization (max AUC = 0.9881; max AUPR=0.9826), and further confirm its real-world predictive performance through case studies.