MLMD: Metric Learning for Predicting MiRNA-Disease Associations

Abstract

The crucial roles played by microRNAs (miRNAs) in regulating various biological functions and in disease incidence have been reported continuously over the past decades. Therefore, the identification of novel disease-related miRNAs could help in understanding human disease etiology and pathogenesis further. Due to the involvement of high cost and more time in clinical experiments, development of accurate and feasible computational models is considered highly significant. Here, we aim to present a novel computational model of metric learning for predicting miRNA-disease associations (MLMD). MLMD aims at learning miRNA-disease metric to unravel not only novel disease-related miRNAs but also the miRNA-miRNA and disease-disease similarities. Comprehensive experimental results clearly proved the outstanding performance of MLMD compared to several state-of-the-art methods. MLMD achieved a reliable AUC score of 0.9106 and 0.8786 in the framework of global and local leave-one-out cross validations (LOOCV), respectively. Furthermore, we implemented case studies on two major human cancers (breast cancer and lung cancer) for comparative analysis with already known disease-related miRNAs. Results revealed the top 50 potential candidates were all disease-related miRNAs based on human public databases and literature analysis. We conclude that MLMD could not only serve as practical and feasible framework for inferring potential miRNA-disease associations, but also provide clues for understanding the human complex diseases.

Keywords

• miRNA
• disease
• metric learning
• latent vector
• omics data integration
• biomarker