GATNNCDA: A Method Based on Graph Attention Network and Multi-Layer Neural Network for Predicting circRNA-Disease Associations
Circular RNAs (circRNAs) are a new class of endogenous non-coding RNAs with covalent closed loop structure. Researchers have revealed that circRNAs play an important role in human diseases. As experimental identification of interactions between circRNA and disease is time-consuming and expensive, ef...
Main Authors: | Cunmei Ji, Zhihao Liu, Yutian Wang, Jiancheng Ni, Chunhou Zheng |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-08-01
|
Series: | International Journal of Molecular Sciences |
Subjects: | |
Online Access: | https://www.mdpi.com/1422-0067/22/16/8505 |
Similar Items
-
GATCDA: Predicting circRNA-Disease Associations Based on Graph Attention Network
by: Chen Bian, et al.
Published: (2021-05-01) -
Prediction of circRNA-Disease Associations Based on the Combination of Multi-Head Graph Attention Network and Graph Convolutional Network
by: Ruifen Cao, et al.
Published: (2022-07-01) -
CRPGCN: predicting circRNA-disease associations using graph convolutional network based on heterogeneous network
by: Zhihao Ma, et al.
Published: (2021-11-01) -
HMCDA: a novel method based on the heterogeneous graph neural network and metapath for circRNA-disease associations prediction
by: Shiyang Liang, et al.
Published: (2023-09-01) -
Identification of potentially functional circRNAs and prediction of the circRNA-miRNA-hub gene network in mice with primary blast lung injury
by: Qianying Lu, et al.
Published: (2023-10-01)