Factor graph-aggregated heterogeneous network embedding for disease-gene association prediction
| Autor: | Yadong Wang, Chen Huang, Ming He, Bo Liu, Junyi Li |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Computer science
Association (object-oriented programming) Heterogeneous network 02 engineering and technology computer.software_genre lcsh:Computer applications to medicine. Medical informatics Biochemistry 03 medical and health sciences Structural Biology 020204 information systems 0202 electrical engineering electronic engineering information engineering Humans Disease Genetic Predisposition to Disease Factorization Molecular Biology lcsh:QH301-705.5 030304 developmental biology Interpretability 0303 health sciences Applied Mathematics Function (mathematics) Graph neural network Semantics Computer Science Applications Variety (cybernetics) Genes lcsh:Biology (General) Scalability Embedding lcsh:R858-859.7 Data mining computer Disease-gene association prediction Factor graph Research Article |
| Zdroj: | BMC Bioinformatics, Vol 22, Iss 1, Pp 1-15 (2021) BMC Bioinformatics |
| ISSN: | 1471-2105 |
| Popis: | 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 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. Conclusions Experimental 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. |
| Databáze: | OpenAIRE |
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