Application of Bidirectional Generative Adversarial Networks to Predict Potential miRNAs Associated With Diseases.

ABSTRACT: Substantial evidence has shown that microRNAs are crucial for biological processes within complex human diseases. Identifying the association of miRNA-disease pairs will contribute to accelerating the discovery of potential biomarkers and pathogenesis. Researchers began to focus on constructing computational models to facilitate the progress of disease pathology and clinical medicine by identifying the potential disease-related miRNAs. However, most existing computational methods are expensive, and their use is limited to unobserved relationships for unknown miRNAs (diseases) without association information. In this manuscript, we proposed a creatively semi-supervised model named bidirectional generative adversarial network for miRNA-disease association prediction (BGANMDA). First, we cons

SUBMITTER: Xu L 

PROVIDER: S-EPMC9314862 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature