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GATCDA: Predicting circRNA-Disease Associations Based on Graph Attention Network
- Source :
- Cancers, Volume 13, Issue 11, Cancers, Vol 13, Iss 2595, p 2595 (2021)
- Publication Year :
- 2021
- Publisher :
- MDPI, 2021.
-
Abstract
- Simple Summary CircRNAs (circular RNAs), a novel kind of non-coding RNAs, play a regulatory role in cellular processes. A growing number of biological experiments has proved that circRNAs can be used as biomarkers and therapeutic targets of some cancers. As the time and financial costs of biological experiments are high, computational methods have become a better way to predict the associations between circRNAs and diseases. Graph attention network was first applied to predict circRNA-disease associations with multiple similarities of data in this study. The circRNA–miRNA interactions and disease-mRNA interactions were adopted to construct features. The computational method proposed in this study has improved the prediction performance. 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.
- Subjects :
- 0301 basic medicine
Cancer Research
Mechanism (biology)
Computer science
Neoplasms. Tumors. Oncology. Including cancer and carcinogens
Computational biology
Disease
Article
graph attention network
circRNA–miRNA–mRNA axis
03 medical and health sciences
030104 developmental biology
0302 clinical medicine
Oncology
Similarity (network science)
030220 oncology & carcinogenesis
Attention network
Graph (abstract data type)
circRNA–disease association
RC254-282
Subjects
Details
- Language :
- English
- ISSN :
- 20726694
- Volume :
- 13
- Issue :
- 11
- Database :
- OpenAIRE
- Journal :
- Cancers
- Accession number :
- edsair.doi.dedup.....af8fb504b331e4fec9ba633f7499818d