Unbiased microRNA-Disease Association Prediction Using ICD-11 Codes and Negative Sampling.

ABSTRACT: We developed a computational model, called "Unbiased microRNA-disease association predictor (UBMDA)," to predict microRNA-disease associations. UBMDA has two major differences from those reported previously. First, we did not apply a similarity-based feature extraction method, which is the main basis of previous studies. Instead, we used International Classification of Diseases 11th Revision disease codes and microRNA nucleotide sequences as input features. Thus, UBMDA can be applied to newly discovered or poorly studied microRNAs and diseases. Second, we constructed an appropriate negative sample dataset. A positive sample dataset consisting of microRNAs and diseases pairs with proven associations between microRNAs and diseases is publicly available. However, datasets reporting no associa

SUBMITTER: Chang M 

PROVIDER: S-EPMC12661550 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature