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Article

GraphMS: Drug Target Prediction Using Graph Representation Learning with Substructures

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Department of Computer Science and Technology, Dalian University of Technology, DaLian 116000, China
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Institute of Economics and Management, Dongbei University of Finance and Economics, DaLian 116000, China
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Artificial Intelligence Group, Baidu Inc., Beijing 100089, China
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Department of Health Technology Assessmen, China National Health Development Research Center, Beijing 100089, China
*
Authors to whom correspondence should be addressed.
Academic Editor: Fabio La Foresta
Appl. Sci. 2021, 11(7), 3239; https://doi.org/10.3390/app11073239
Received: 1 March 2021 / Revised: 30 March 2021 / Accepted: 1 April 2021 / Published: 4 April 2021
(This article belongs to the Section Computing and Artificial Intelligence)
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 proposed our model named GraphMS. 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–the–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 0.847. 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. View Full-Text
Keywords: graph embedding; link prediction; mutual information; subgraph graph embedding; link prediction; mutual information; subgraph
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MDPI and ACS Style

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. https://doi.org/10.3390/app11073239

AMA Style

Cheng S, Zhang L, Jin B, Zhang Q, Lu X, You M, Tian X. GraphMS: Drug Target Prediction Using Graph Representation Learning with Substructures. Applied Sciences. 2021; 11(7):3239. https://doi.org/10.3390/app11073239

Chicago/Turabian Style

Cheng, Shicheng, Liang Zhang, Bo Jin, Qiang Zhang, Xinjiang Lu, Mao You, and Xueqing Tian. 2021. "GraphMS: Drug Target Prediction Using Graph Representation Learning with Substructures" Applied Sciences 11, no. 7: 3239. https://doi.org/10.3390/app11073239

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