MMAG is proposed, a novel framework that formulates miRNA–disease association prediction as a meta-conditional distribution alignment problem on multi-scale biological graphs and offers a promising strategy for broader biological network inference tasks.
Abstract
Introduction Identifying potential miRNA–disease associations is essential for clarifying the molecular basis of complex diseases and accelerating the discovery of diagnostic biomarkers and therapeutic targets. However, the performance of existing computational methods is often limited by sparse biological interaction networks, highly imbalanced disease distributions, and the small number of experimentally validated associations. Methods To address these challenges, we propose MMAG, a novel framework that formulates miRNA–disease association prediction as a meta-conditional distribution alignment problem on multi-scale biological graphs. MMAG integrates three complementary components. First, a multi-scale representation learning module captures hierarchical biological information from local topological connectivity, mesoscopic functional organization, and global spectral structure. Second, a meta-learning strategy models each disease as an individual task, enabling the model to learn disease-specific prototype representations from support samples and adapt effectively to few-shot settings. Third, a conditional adversarial alignment mechanism reduces feature distribution discrepancies across diseases with different data scales, thereby enhancing cross-task knowledge transfer and generalization. Results Extensive experiments demonstrate that MMAG consistently outperforms several state-of-the-art methods under few-shot, long-tailed, and cross-dataset transfer scenarios. Discussion These results indicate that MMAG provides an effective and scalable solution for miRNA–disease association prediction and offers a promising strategy for broader biological network inference tasks.
A novel computational framework based on network fusion and matrix completion for miRNA-disease association prediction, which integrates heterogeneous biological information, including miRNA-disease, lncRNA-disease, and miRNA-lncRNA associations, together with disease semantic similarity and miRNA/lncRNA functional similarity is proposed.
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Drive by the rapid envolution of deep-learning techniques, a large body of biological experiments bas has uncovered extensive associations between microRNAs (miRNAs) and complex human diseases, hig- hlighting the pivotal roles of miRNAs in pathogenesis. Elucidating these associations is essential for understanding disease mechanisms and developing preventive strategies. Traditional wet-lab validati- on, however, is notoriously labor- and resource-intensive, creating an urgent demand for efficient computational tools that can prioritize the most promising miRNA–disease candidates. Existing predictors predominantly rely on a single category of handcrafted features, thereby overlooking the complementary information embedded in multiple, heterogeneous data sources. Although a few recent attempts integrate diverse features, they usually exploit only a limited subset and fail to capture the intricate, non-linear relationships among them. To address these limitations, we propose MFCAMNet, a Multi-Feature fusion and Cross-Self-Attention model for MiRNA–Disease association prediction. Firstly, we construct multiple similarity matrices and employ two independent autoencoders with multi-source feature attention to obtain deep features of miRNA and disease to extract the inherent relationships between multiple features. Secondly, the proposed model employs element-level addition, element-level multiplication, and concatenation operations to generate miRNA-disease pair features with rich information. Finally, we use the encoder structure of the transformer to fuse the three deep features and predict all potential miRNA disease associations. We conducted comprehensive evaluations on the public HMDD v2.0 and HMDD v3.2 benchmark datasets. MFCAMNet achieved average AUCs of 0.9455 and 0.9420 under 5-fold and 10-fold cross-validation on HMDD v2.0, respectively, and an AUC of 0.9578 under 5-fold cross-validation on HMDD v3.2, outperforming state-of-the-art competitors. Case studies on breast, esophageal, and lung cancers further corroborate the reliability and practical utility of the proposed method.
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