HMCDA: a novel method based on the heterogeneous graph neural network and metapath for circRNA-disease associations prediction.

ABSTRACT: Circular RNA (CircRNA) is a type of non-coding RNAs in which both ends are covalently linked. Researchers have demonstrated that many circRNAs can act as biomarkers of diseases. However, traditional experimental methods for circRNA-disease associations identification are labor-intensive. In this work, we propose a novel method based on the heterogeneous graph neural network and metapaths for circRNA-disease associations prediction termed as HMCDA. First, a heterogeneous graph consisting of circRNA-disease associations, circRNA-miRNA associations, miRNA-disease associations and disease-disease associations are constructed. Then, six metapaths are defined and generated according to the biomedical pathways. Afterwards, the entity content transformation, intra-metapath and inter-metapath aggre

SUBMITTER: Liang S 

PROVIDER: S-EPMC10494331 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature