GATCDA: Predicting circRNA-Disease Associations Based on Graph Attention Network.
Cancers (Basel)
; 13(11)2021 May 25.
Article
em En
| MEDLINE
| ID: mdl-34070678
ABSTRACT
CircRNAs (circular RNAs) are a class of non-coding RNA molecules with a closed circular structure. CircRNAs are closely related to the occurrence and development of diseases. Due to the time-consuming nature of biological experiments, computational methods have become a better way to predict the interactions between circRNAs and diseases. In this study, we developed a novel computational method called GATCDA utilizing a graph attention network (GAT) to predict circRNA-disease associations with disease symptom similarity, network similarity, and information entropy similarity for both circRNAs and diseases. GAT learns representations for nodes on a graph by an attention mechanism, which assigns different weights to different nodes in a neighborhood. Considering that the circRNA-miRNA-mRNA axis plays an important role in the generation and development of diseases, circRNA-miRNA interactions and disease-mRNA interactions were adopted to construct features, in which mRNAs were related to 88% of miRNAs. As demonstrated by five-fold cross-validation, GATCDA yielded an AUC value of 0.9011. In addition, case studies showed that GATCDA can predict unknown circRNA-disease associations. In conclusion, GATCDA is a useful method for exploring associations between circRNAs and diseases.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
Cancers (Basel)
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
China