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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 7
2020-2024,2057

Drug synergy prediction algorithm based on supervised multi-view graph neural network

Hao Zhifeng1,2
Zhan Jianming1
Cai Ruichu1
1. School of Computer Science, Guangdong University of Technology, Guangzhou 510006, China
2. School of Science, Shantou University, Shantou Guangdong 515063, China
DOI: 10.19734/j.issn.1001-3695.2022.01.0004

Abstract

Drug combination therapy has important application value in the cancer treatment. Predicting drug synergistic combinations through computational methods can provide targeted guidance for biological research, thereby improving research efficiency and reducing experimental costs. This paper proposed a multi-view graph neural network for drug synergy prediction to solve the problem of existing algorithms lack effective drug interaction modeling methods and could not consider the relationship between cell lines. Firstly, the algorithm used variational graph auto-encoder to learn the drug embedding of a specific cell line. Then, the algorithm integrated the drug information of other cell lines in the same tissue through a multi-view framework to improve the reliability of the drug embedding. Finally, the algorithm used the known drug combination scores as a supervisory signal to supervised training of the model to achieve reliable prediction of drug synergy effects. The experimental results on the DrugComb dataset demonstrate the effectiveness of the method.

Foundation Support

国家自然科学基金资助项目(61876043,61976052)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.01.0004
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 7
Section: Algorithm Research & Explore
Pages: 2020-2024,2057
Serial Number: 1001-3695(2022)07-015-2020-05

Publish History

[2022-03-08] Accepted Paper
[2022-07-05] Printed Article

Cite This Article

郝志峰, 詹健明, 蔡瑞初. 基于有监督的多视角图神经网络的药物组合协同预测算法 [J]. 计算机应用研究, 2022, 39 (7): 2020-2024,2057. (Hao Zhifeng, Zhan Jianming, Cai Ruichu. Drug synergy prediction algorithm based on supervised multi-view graph neural network [J]. Application Research of Computers, 2022, 39 (7): 2020-2024,2057. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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