Disentangled similarity graph attention heterogeneous biological memory network for predicting disease-associated miRNAs.

ABSTRACT:

Background

The association between MicroRNAs (miRNAs) and diseases is crucial in treating and exploring many diseases or cancers. Although wet-lab methods for predicting miRNA-disease associations (MDAs) are effective, they are often expensive and time-consuming. Significant advancements have been made using Graph Neural Network-based methods (GNN-MDAs) to address these challenges. However, these methods still face limitations, such as not considering nodes' deep-level similarity associations and hierarchical learning patterns. Additionally, current models do not retain the memory of previously learned heterogeneous historical information about miRNAs or diseases, only focusing on parameter learning without the capability to remember heterogeneous associations.

Results

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SUBMITTER: Liu Y 

PROVIDER: S-EPMC11610307 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature