Cargando…
Using Graph Attention Network and Graph Convolutional Network to Explore Human CircRNA–Disease Associations Based on Multi-Source Data
Cumulative research studies have verified that multiple circRNAs are closely associated with the pathogenic mechanism and cellular level. Exploring human circRNA–disease relationships is significant to decipher pathogenic mechanisms and provide treatment plans. At present, several computational mode...
Autores principales: | Li, Guanghui, Wang, Diancheng, Zhang, Yuejin, Liang, Cheng, Xiao, Qiu, Luo, Jiawei |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8859418/ https://www.ncbi.nlm.nih.gov/pubmed/35198012 http://dx.doi.org/10.3389/fgene.2022.829937 |
Ejemplares similares
-
Prediction of circRNA-Disease Associations Based on the Combination of Multi-Head Graph Attention Network and Graph Convolutional Network
por: Cao, Ruifen, et al.
Publicado: (2022) -
GCNCMI: A Graph Convolutional Neural Network Approach for Predicting circRNA-miRNA Interactions
por: He, Jie, et al.
Publicado: (2022) -
Predicting miRNA-disease associations based on graph attention network with multi-source information
por: Li, Guanghui, et al.
Publicado: (2022) -
GATCDA: Predicting circRNA-Disease Associations Based on Graph Attention Network
por: Bian, Chen, et al.
Publicado: (2021) -
CRPGCN: predicting circRNA-disease associations using graph convolutional network based on heterogeneous network
por: Ma, Zhihao, et al.
Publicado: (2021)