Relation-aware Heterogeneous Graph Transformer based drug repurposing

作者：

Highlights：

• A novel neural model, called RHGT, is proposed for drug repurposing.

• RHGT characterizes the heterogeneity of the network at node level and edge level.

• A fine-grained method is developed to learn edge type embedding.

• RHGT achieves state-of-art performance in CTD and TTD datasets.

摘要

•A novel neural model, called RHGT, is proposed for drug repurposing.•RHGT characterizes the heterogeneity of the network at node level and edge level.•A fine-grained method is developed to learn edge type embedding.•RHGT achieves state-of-art performance in CTD and TTD datasets.

论文关键词：Drug repurposing,Graph neural network,Graph transformer,Link prediction,Heterogeneous network

论文评审过程：Received 30 April 2021, Revised 21 October 2021, Accepted 27 October 2021, Available online 16 November 2021, Version of Record 19 November 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.116165