Predicting Drug Synergy and Discovering New Drug Combinations Based on a Graph Autoencoder and Convolutional Neural Network-Reference-Cited by-同舟云学术

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Predicting Drug Synergy and Discovering New Drug Combinations Based on a Graph Autoencoder and Convolutional Neural Network

Published:2023-03-21 Issue:2 Volume:15 Page:316-330
ISSN:1913-2751
Container-title:Interdisciplinary Sciences: Computational Life Sciences
language:en
Short-container-title:Interdiscip Sci Comput Life Sci

Author:

Li Huijun,Zou Lin,Kowah Jamal A. H.,He Dongqiong,Wang Lisheng,Yuan Mingqing,Liu Xu^ORCID

Funder

National Natural Science Foundation of China

Guangxi Innovation-Driven Development Special Fund Project

Guangxi Key Research and Development Program

Guangxi Key Laboratory of Traditional Chinese Medicine Quality Standards

Key Laboratory of Trusted Software

Publisher

Springer Science and Business Media LLC

Subject

Health Informatics,Computer Science Applications,General Biochemistry, Genetics and Molecular Biology

Link

https://link.springer.com/content/pdf/10.1007/s12539-023-00558-y.pdf

Reference45 articles.

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2. Cuvitoglu A, Zhou JX, Huang S, Isik Z (2019) Predicting drug synergy for precision medicine using network biology and machine learning. J Bioinform Comput Biol 17(2):1950012. https://doi.org/10.1142/S0219720019500124

3. Wildenhain J, Spitzer M, Dolma S, Jarvik N, White R, Roy M, Griffiths E, Bellows DS, Wright GD, Tyers M (2015) Prediction of synergism from chemical-genetic interactions by machine learning. Cell Syst 1(6):383–395. https://doi.org/10.1016/j.cels.2015.12.003

4. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444. https://doi.org/10.1038/nature14539

5. Ali MNY, Rahman ML, Chaki J, Dey N, Santosh KC (2021) Machine translation using deep learning for universal networking language based on their structure. Int J Mach Learn Cybern 12(8):2365–2376. https://doi.org/10.1007/s13042-021-01317-5

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