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

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

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Abstract Background Exploring the relationship between disease and gene is of great TOTAL E 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 Stock 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.