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

BMC Bioinformatics

Bo Liu¹  Junyi Li²  Chen Huang²  Ming He²  Yadong Wang³ 

[1] Center for Bioinformatics, School of Computer Science and Technology, Harbin Institute of Technology, 150001, Harbin, Heilongjiang, China;School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), 518055, Shenzhen, Guangdong, China;School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), 518055, Shenzhen, Guangdong, China;Center for Bioinformatics, School of Computer Science and Technology, Harbin Institute of Technology, 150001, Harbin, Heilongjiang, China;

关键词: Disease-gene association prediction;    Heterogeneous network;    Graph neural network;    Factorization;   

DOI  :  10.1186/s12859-021-04099-3

来源: Springer

【 摘 要 】

BackgroundExploring 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.ResultsMany existing methods cannot fully utilize the multi-dimensional biological entity relationship to predict disease-gene association due to multi-source heterogeneous data. This paper proposes FactorHNE, a factor graph-aggregated heterogeneous network embedding method for disease-gene association prediction, which captures a variety of semantic relationships between the heterogeneous nodes by factorization. It produces different semantic factor graphs and effectively aggregates a variety of semantic relationships, by using end-to-end multi-perspectives loss function to optimize model. Then it produces good nodes embedding to prediction disease-gene association.ConclusionsExperimental verification and analysis show FactorHNE has better performance and scalability than the existing models. It also has good interpretability and can be extended to large-scale biomedical network data analysis.

【 授权许可】

CC BY