BMC Bioinformatics | |
Factor graph-aggregated heterogeneous network embedding for disease-gene association prediction | |
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
【 预 览 】
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RO202107023831555ZK.pdf | 1507KB | download |