Title:MDAlmc: A Novel Low-rank Matrix Completion Model for MiRNADisease
Association Prediction by Integrating Similarities among MiRNAs
and Diseases
Volume: 23
Issue: 4
关键词:
MiRNA-疾病关联,低等级矩阵完成,5倍交叉验证,AUROC,MDA1mc,AUPRC。
摘要:
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.
