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Current Gene Therapy

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ISSN (Print): 1566-5232
ISSN (Online): 1875-5631

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Research Article

MDAlmc: A Novel Low-rank Matrix Completion Model for MiRNADisease Association Prediction by Integrating Similarities among MiRNAs and Diseases

Author(s): Kun Wang, Junlin Xu, Geng Tian, Yang Li, Xueying Zeng* and Jialiang Yang*

Volume 23, Issue 4, 2023

Published on: 24 May, 2023

Page: [316 - 327] Pages: 12

DOI: 10.2174/1566523223666230419101405

Price: $65

Abstract

Introduction: The importance of microRNAs (miRNAs) has been emphasized by an increasing number of studies, and it is well-known that miRNA dysregulation is associated with a variety of complex diseases. Revealing the associations between miRNAs and diseases are essential to disease prevention, diagnosis, and treatment.

Methods: However, traditional experimental methods in validating the roles of miRNAs in diseases could be very expensive, labor-intensive and time-consuming. Thus, there is a growing interest in predicting miRNA-disease associations by computational methods. Though many computational methods are in this category, their prediction accuracy needs further improvement for downstream experimental validation. In this study, we proposed a novel model to predict miRNA-disease associations by low-rank matrix completion (MDAlmc) integrating miRNA functional similarity, disease semantic similarity, and known miRNA-disease associations. In the 5-fold cross-validation, MDAlmc achieved an average AUROC of 0.8709 and AUPRC of 0.4172, better than those of previous models.

Results: Among the case studies of three important human diseases, the top 50 predicted miRNAs of 96% (breast tumors), 98% (lung tumors), and 90% (ovarian tumors) have been confirmed by previous literatures. And the unconfirmed miRNAs were also validated to be potential disease-associated miRNAs.

Conclusion: MDAlmc is a valuable computational resource for miRNA–disease association prediction.

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