MiRdisNET: Discovering microRNA biomarkers that are associated with diseases utilizing biological knowledge-based machine learning.

ABSTRACT: During recent years, biological experiments and increasing evidence have shown that microRNAs play an important role in the diagnosis and treatment of human complex diseases. Therefore, to diagnose and treat human complex diseases, it is necessary to reveal the associations between a specific disease and related miRNAs. Although current computational models based on machine learning attempt to determine miRNA-disease associations, the accuracy of these models need to be improved, and candidate miRNA-disease relations need to be evaluated from a biological perspective. In this paper, we propose a computational model named miRdisNET to predict potential miRNA-disease associations. Specifically, miRdisNET requires two types of data, i.e., miRNA expression profiles and known disease-miRNA asso

SUBMITTER: Jabeer A 

PROVIDER: S-EPMC9877296 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature