Predicting CircRNA-Disease Associations via Feature Convolution Learning With Heterogeneous Graph Attention Network-Reference-Cited by-同舟云学术

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Predicting CircRNA-Disease Associations via Feature Convolution Learning With Heterogeneous Graph Attention Network

Published:2023-06 Issue:6 Volume:27 Page:3072-3082
ISSN:2168-2194
Container-title:IEEE Journal of Biomedical and Health Informatics
language:
Short-container-title:IEEE J. Biomed. Health Inform.

Author:

Peng Li¹^ORCID,Yang Cheng¹,Chen Yifan²,Liu Wei³^ORCID

Affiliation:

1. School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, China

2. College of Information Science and Engineering, Hunan University, Changsha, China

3. School of Computer Science, Xiangtan University, Xiangtan, China

Funder

National Natural Science Foundation of China

Scientific Research Project of Hunan Education Department

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Health Information Management,Electrical and Electronic Engineering,Computer Science Applications,Health Informatics

Link

http://xplorestaging.ieee.org/ielx7/6221020/10144459/10079096.pdf?arnumber=10079096

Reference60 articles.

1. Circular RNAs and complex diseases: from experimental results to computational models

2. A novel information fusion strategy based on a regularized framework for identifying disease-related microRNAs

3. Prediction of potential miRNA–disease associations based on stacked autoencoder

4. Microarray profiles reveal that circular RNA hsa_circ_0007385 functions as an oncogene in non-small cell lung cancer tumorigenesis

5. CirRNAPL: A web server for the identification of circRNA based on extreme learning machine

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1. Identifying circRNA-disease association based on relational graph attention network and hypergraph attention network;Analytical Biochemistry;2024-11

2. Dual-neighbourhood information aggregation and feature fusion for prediction of miRNA–disease association;Computers in Biology and Medicine;2024-10

3. GLNNMDA: a multimodal prediction model for microbe-drug associations based on global and local features;Scientific Reports;2024-09-06

4. Graph Attention Networks: A Comprehensive Review of Methods and Applications;Future Internet;2024-09-03

5. An ensemble approach for circular RNA-disease association prediction using variational autoencoder and genetic algorithm;Journal of Bioinformatics and Computational Biology;2024-08-31

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