MicroRNAs (miRNAs) play a key role in the prevention, diagnosis, and treatment of complex diseases. However, identifying miRNA-disease associations (MDAs) through traditional methods is costly and time-consuming. Recent studies have reported numerous validated MDAs, forming the basis for the prediction of new MDAs using computational methods. In this study, we propose SAETNMDA, a computational method that applies fast kernel learning (FKL) and variant triplet networks to predict MDAs. First, miRNA and disease similarities are integrated into two kernels via FKL to enrich biological data. Next, feature representations are obtained by applying stacked autoencoders (SAEs) and triplet networks, enabling the identification of associated pairs by mapping them to nearby locations in the embedding space, while unassociated ones are mapped distantly. Finally, we utilize XGBoost (Extreme Gradient Boosting) to obtain predictive scores for MDAs from these features. SAETNMDA’s performance is evaluated with 5-fold cross-validation (5-fold-CV) and compared with other methods. It achieves the highest AUC and AUPR (0.9419, 0.4749 for HMDD v2.0; 0.9496, 0.5355 for HMDD v3.2, respectively). The performance is also validated on an independent dataset and de novo miRNAs, with SAETNMDA achieving the highest AUC and AUPR in all validations. Case studies also demonstrate the robust predictive capability of our method, with the top 50 predicted miRNAs validated for each of the three diseases. These results highlight SAETNMDA as an efficient model for MDA prediction. SAETNMDA’s source code is available at https://github.com/npxquynhdhsp/SAETNMDA.

As the global population ages, the prevalence of Alzheimer’s Disease (AD) has been steadily increasing. Traditional Chinese medicine, with abundant ingredients and multi-targeting, has shown promising anti-AD effects. However, the complex mechanism of herbal actions makes it challenging to discover effective herbal prescriptions for AD. In this study, we propose an herbal prescription recommendation approach for AD based on network propagation and reinforcement learning. A target-ingredient-herb network is constructed, and network propagation is used to obtain the network score (N_score) of an herb. The empirical score (E_score) of the herb pair is calculated based on known prescriptions. The herbal prescription score (HP_score) is then calculated based on N_score and E_score. Finally, reinforcement learning is combined with HP_score to infer effective prescriptions. By validating the predicted prescriptions, we identify the targets of the herbs and AD-related proteins from the database, refining these results through cross-analysis. Core targets are determined using protein-protein interaction network analysis. Gene ontology and Kyoto encyclopedia of genes and genomes analyses explore biological roles of core targets. Molecular docking confirms interactions between actives and targets. Results show optimal herbal formulations act effectively on targets. These may offer strategies to optimize prescriptions, therapies, and drug development.