Back to Search Start Over

KGANCDA: predicting circRNA-disease associations based on knowledge graph attention network.

Authors :
Lan, Wei
Dong, Yi
Chen, Qingfeng
Zheng, Ruiqing
Liu, Jin
Pan, Yi
Chen, Yi-Ping Phoebe
Source :
Briefings in Bioinformatics; Jan2022, Vol. 23 Issue 1, p1-13, 13p
Publication Year :
2022

Abstract

Increasing evidences have proved that circRNA plays a significant role in the development of many diseases. In addition, many researches have shown that circRNA can be considered as the potential biomarker for clinical diagnosis and treatment of disease. Some computational methods have been proposed to predict circRNA-disease associations. However, the performance of these methods is limited as the sparsity of low-order interaction information. In this paper, we propose a new computational method (KGANCDA) to predict circRNA-disease associations based on knowledge graph attention network. The circRNA-disease knowledge graphs are constructed by collecting multiple relationship data among circRNA, disease, miRNA and lncRNA. Then, the knowledge graph attention network is designed to obtain embeddings of each entity by distinguishing the importance of information from neighbors. Besides the low-order neighbor information, it can also capture high-order neighbor information from multisource associations, which alleviates the problem of data sparsity. Finally, the multilayer perceptron is applied to predict the affinity score of circRNA-disease associations based on the embeddings of circRNA and disease. The experiment results show that KGANCDA outperforms than other state-of-the-art methods in 5-fold cross validation. Furthermore, the case study demonstrates that KGANCDA is an effective tool to predict potential circRNA-disease associations. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
14675463
Volume :
23
Issue :
1
Database :
Complementary Index
Journal :
Briefings in Bioinformatics
Publication Type :
Academic Journal
Accession number :
155892377
Full Text :
https://doi.org/10.1093/bib/bbab494