MHCLMDA: multihypergraph contrastive learning for miRNA-disease association prediction.

ABSTRACT: The correct prediction of disease-associated miRNAs plays an essential role in disease prevention and treatment. Current computational methods to predict disease-associated miRNAs construct different miRNA views and disease views based on various miRNA properties and disease properties and then integrate the multiviews to predict the relationship between miRNAs and diseases. However, most existing methods ignore the information interaction among the views and the consistency of miRNA features (disease features) across multiple views. This study proposes a computational method based on multiple hypergraph contrastive learning (MHCLMDA) to predict miRNA-disease associations. MHCLMDA first constructs multiple miRNA hypergraphs and disease hypergraphs based on various miRNA similarities and di

SUBMITTER: Peng W 

PROVIDER: S-EPMC10796254 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature