An Enhanced LRMC Method for Drug Repositioning via GCN-based HIN Embedding
作者： Zhi Tang , Xiaoqing Lyu , Bei Wang , Zhenming Liu , Yifan Wang
DOI: 10.1109/BIBM49941.2020.9313191
关键词:
摘要: Drug repositioning has received ever-increasing attention in the field of drug discovery over last few years. However, high efficient prediction methods taking full advantage heterogeneous information networks (HINs) still deserves further research. To this end, paper proposes an approach for via integrating HINs embedding and link more potential drug-target interactions. utilize multiple side information, we introduce a graph convolutional network (GCN) based method HINs. The obtained drug-related target-related is adopted to improve low-rank matrix completion (LRMC) model. Moreover, regulation alleviating noise negative samples designed enhance optimization LRMC. experiments conducted on comparative database demonstrate that proposed effective than existing approaches repositioning.
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