AMFCL: Predicting miRNA-Disease Associations Through Adaptive Multi-source Modality Fusion and Contrastive Learning.

Dysregulation of microRNAs (miRNAs) is a cause of progression in numerous diseases. Uncovering miRNA-disease associations (MDAs) is essential for discovering new biomarkers. Nonetheless, in contrast to conventional biological approaches, advanced computational approaches are typically more rapid and cost-effective. However, most computational methods still face several challenges: (i) integrating multi-source information (MSI); (ii) optimizing feature fusion; (iii) mitigating over-smoothing in graph-based models. This paper introduces a novel model, AMFCL. To encapsulate the miRNA-disease relationships, three types of networks are first constructed. After that, the node representations are learned via multi-layer graph sample and aggregate (GraphSAGE). An adaptive fusion mechanism (AFM) dynamically assigns weights to feature representations to optimize the fusion process. Additionally, a residual connection is used to combat the over-smoothing effect that occurs in graph-based models. The robustness of miRNA and disease embeddings is improved by contrastive learning (CL). Lastly, a multi-layer perceptron (MLP) has all feature embeddings fed into it for the computation of MDA scores. The corresponding experimental results show remarkable improvements in AMFCL compared to advanced models. Moreover, relevant case studies systematically validate the approach's effectiveness in identifying unknown MDAs.