IPiDA-LGE: a local and global graph ensemble learning framework for identifying piRNA-disease associations.

ABSTRACT:

Background

Exploring piRNA-disease associations can help discover candidate diagnostic or prognostic biomarkers and therapeutic targets. Several computational methods have been presented for identifying associations between piRNAs and diseases. However, the existing methods encounter challenges such as over-smoothing in feature learning and overlooking specific local proximity relationships, resulting in limited representation of piRNA-disease pairs and insufficient detection of association patterns.

Results

In this study, we propose a novel computational method called iPiDA-LGE for piRNA-disease association identification. iPiDA-LGE comprises two graph convolutional neural network modules based on local and global piRNA-disease graphs, aimed at capturing specific and genera

SUBMITTER: Wei H 

PROVIDER: S-EPMC12065364 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature