Prediction of miRNA-disease Associations by Deep Matrix Decomposition Method based on Fused Similarity Information

Abstract

Aim: MicroRNAs (miRNAs), pivotal regulators in various biological processes, are closely linked to human diseases. This study aims to propose a computational model, SIDMF, for predicting miRNA-disease associations. Background: Computational methods have proven efficient in predicting miRNA-disease associations, leveraging functional similarity and network-based inference. Machine learning techniques, including support vector machines, semi-supervised algorithms, and deep learning models, have gained prominence in this domain. Objective: Develop a computational model that integrates disease semantic similarity and miRNA functional similarity within a deep matrix factorization framework to predict potential associations between miRNAs and diseases accurately. Methods: SIDMF, introduced in this study, integrates disease semantic similarity and miRNA functional similarity within a deep matrix factorization framework. Through the reconstruction of the miRNA-disease association matrix, SIDMF predicts potential associations between miRNAs and diseases. Results: The performance of SIDMF was evaluated using global Leave-One-Out Cross-Validation (LOOCV) and local LOOCV, achieving high Area Under the Curve (AUC) values of 0.9536 and 0.9404, respectively. Comparative analysis against other methods demonstrated the superior performance of SIDMF. Case studies on breast cancer, esophageal cancer, and prostate cancer further validated SIDMF's predictive accuracy, with a substantial percentage of the top 50 predicted miRNAs confirmed in relevant databases. Conclusion: SIDMF emerges as a promising computational model for predicting potential associations between miRNAs and diseases. Its robust performance in global and local evaluations, along with successful case studies, underscores its potential contributions to disease prevention, diagnosis, and treatment.