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 de-veloping corresponding therapeutic measures. The prediction of dis-ease-gene association by computational methods accelerates the pro-cess. Results: Many existing methods cannot fully utilize the multi-dimen-sional biological entity relationship to predict disease-gene association due to multi-source heterogeneous data. This paper proposes Fac-torHNE, a factor graph-aggregated heterogeneous network embedding method for disease-gene association prediction, which captures a vari-ety of semantic relationships between the heterogeneous nodes by fac-torization. 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 can be extended to large-scale biomedical net-work data analysis.