Factor graph-aggregated heterogeneous network embedding for disease-gene association prediction

Factor graph-aggregated heterogeneous network embedding for disease-gene association prediction

Background: Exploring the relationship between disease and gene is of great significance for understanding the pathogenesis of disease and developing corresponding therapeutic measures. The prediction of disease-gene association by computational methods accelerates the process. Results: Many existin...

Full description

Bibliographic Details
Main Authors: He, M. (Author), Huang, C. (Author), Li, J. (Author), Liu, B. (Author), Wang, Y. (Author)
Format: Article
Language:English
Published: BioMed Central Ltd 2021
Subjects:
Biological entities
Disease
Disease-gene association prediction
diseases
Embeddings
Experimental verification
Factorization
Forecasting
gene
Genes
genetic predisposition
Genetic Predisposition to Disease
genetics
Graph neural network
Heterogeneous data
Heterogeneous network
Heterogeneous networks
Heterogeneous nodes
human
Humans
Multi dimensional
Network embedding
Performance and scalabilities
Semantic relationships
semantics
Semantics
Online Access:View Fulltext in Publisher

Internet

View Fulltext in Publisher

Similar Items