CRPGCN: predicting circRNA-disease associations using graph convolutional network based on heterogeneous network

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...

Full description

Bibliographic Details
Main Authors: Deng, L. (Author), Kuang, Z. (Author), Ma, Z. (Author)
Format: Article
Language:English
Published: BioMed Central Ltd 2021
Subjects:
Adjacency matrix
algorithm
Algorithms
CircRNA-disease
Convolution
Convolutional networks
Deep learning
Diagnosis
Experimental methods
Feature matrices
Forecasting
Graph convolutional network
Heterogeneous networks
Heterogenous network
Principal component analysis
Principal-component analysis
Random walk with restart
RNA, Circular
Online Access:View Fulltext in Publisher

Internet

View Fulltext in Publisher

Similar Items