Cargando…
CRPGCN: predicting circRNA-disease associations using graph convolutional network based on heterogeneous network
BACKGROUND: The existing studies show that circRNAs can be used as a biomarker of diseases and play a prominent role in the treatment and diagnosis of diseases. However, the relationships between the vast majority of circRNAs and diseases are still unclear, and more experiments are needed to study t...
Autores principales: | Ma, Zhihao, Kuang, Zhufang, Deng, Lei |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8588735/ https://www.ncbi.nlm.nih.gov/pubmed/34772332 http://dx.doi.org/10.1186/s12859-021-04467-z |
Ejemplares similares
-
GCNCMI: A Graph Convolutional Neural Network Approach for Predicting circRNA-miRNA Interactions
por: He, Jie, et al.
Publicado: (2022) -
DEJKMDR: miRNA-disease association prediction method based on graph convolutional network
por: Gao, Shiyuan, et al.
Publicado: (2023) -
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) -
Fusion of multiple heterogeneous networks for predicting circRNA-disease associations
por: Deng, Lei, et al.
Publicado: (2019) -
GCNCDA: A new method for predicting circRNA-disease associations based on Graph Convolutional Network Algorithm
por: Wang, Lei, et al.
Publicado: (2020)