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 m

SUBMITTER: Long J 

PROVIDER: S-EPMC12881826 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature