基于异质生物图动态表示学习的药物-靶标关系预测OA

To extract the complex relationships between drugs and targets,this paper designs a dynamic representation learning algorithm based on deep heterogeneous graph gated convolutional networks(HGGCN)for biological graph modeling and representation learning.The algorithm combines the merits of the gated channels and feature channels to adaptively model interaction patterns of heterogeneous graphs,enhance the topological structure and semantic information of complex networks based on the fusion,and obtain the discriminative representation of drugs and targets for drug-target interaction mining.Experimental results show that the proposed model outperforms existing drug target interaction prediction methods,and is also an accurate drug target association prediction tool,which could provide the technical support for the precision treatment of complex diseases and network information mining.