Cheng, S.; Zhang, L.; Jin, B.; Zhang, Q.; Lu, X.; You, M.; Tian, X. GraphMS: Drug Target Prediction Using Graph Representation Learning with Substructures. Appl. Sci.2021, 11, 3239.
Cheng, S.; Zhang, L.; Jin, B.; Zhang, Q.; Lu, X.; You, M.; Tian, X. GraphMS: Drug Target Prediction Using Graph Representation Learning with Substructures. Appl. Sci. 2021, 11, 3239.
Cheng, S.; Zhang, L.; Jin, B.; Zhang, Q.; Lu, X.; You, M.; Tian, X. GraphMS: Drug Target Prediction Using Graph Representation Learning with Substructures. Appl. Sci.2021, 11, 3239.
Cheng, S.; Zhang, L.; Jin, B.; Zhang, Q.; Lu, X.; You, M.; Tian, X. GraphMS: Drug Target Prediction Using Graph Representation Learning with Substructures. Appl. Sci. 2021, 11, 3239.
Abstract
The prediction of drug--target interactions is always a key task in the field of drug redirection. However, traditional methods of predicting drug--target interactions are either mediocre or rely heavily on data stacking. In this work, we merged heterogeneous graph information and obtained effective node information and substructure information based on mutual information in graph embeddings. We then learned high quality representations for downstream tasks, and proposed an end--to--end auto--encoder model to complete the task of link prediction. Experimental results show that our method outperforms several state--of--art models. The model can achieve the area under the receiver operating characteristics (AUROC) curve of 0.959 and area under the precise recall curve (AUPR) of 0.848. We found that the mutual information between the substructure and graph--level representations contributes most to the mutual information index in a relatively sparse network. And the mutual information between the node--level and graph--level representations contributes most in a relatively dense network.
Keywords
Graph embedding; Link prediction; Mutual information; Subgraph
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
