PDA-PRGCN: identification of Piwi-interacting RNA-disease associations through subgraph projection and residual scaling-based feature augmentation.

ABSTRACT:

Background

Emerging evidences show that Piwi-interacting RNAs (piRNAs) play a pivotal role in numerous complex human diseases. Identifying potential piRNA-disease associations (PDAs) is crucial for understanding disease pathogenesis at molecular level. Compared to the biological wet experiments, the computational methods provide a cost-effective strategy. However, few computational methods have been developed so far.

Results

Here, we proposed an end-to-end model, referred to as PDA-PRGCN (PDA prediction using subgraph Projection and Residual scaling-based feature augmentation through Graph Convolutional Network). Specifically, starting with the known piRNA-disease associations represented as a graph, we applied subgraph projection to construct piRNA-piRNA and disease-disease

SUBMITTER: Zhang P 

PROVIDER: S-EPMC9843905 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature