Detecting drug communities and predicting comprehensive drug–drug interactions via balance regularized semi-nonnegative matrix factorization-Reference-Cited by-同舟云学术

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Detecting drug communities and predicting comprehensive drug–drug interactions via balance regularized semi-nonnegative matrix factorization

Published:2019-04-08 Issue:1 Volume:11 Page:
ISSN:1758-2946
Container-title:Journal of Cheminformatics
language:en
Short-container-title:J Cheminform

Author:

Shi Jian-Yu^ORCID,Mao Kui-Tao,Yu Hui,Yiu Siu-Ming

Funder

National Natural Science Foundation of China

Seed Foundation of Innovation and Creation for Graduate Students

China National Training Programs of Innovation and Entrepreneurship for Undergraduates

Program of Peak Experience of Northwestern Polytechnical University

Publisher

Springer Science and Business Media LLC

Subject

Library and Information Sciences,Computer Graphics and Computer-Aided Design,Physical and Theoretical Chemistry,Computer Science Applications

Link

http://link.springer.com/content/pdf/10.1186/s13321-019-0352-9.pdf

Reference41 articles.

1. Wienkers LC, Heath TG (2005) Predicting in vivo drug interactions from in vitro drug discovery data. Nat Rev Drug Discov 4(10):825–833

2. Law V, Knox C, Djoumbou Y, Jewison T, Guo AC, Liu Y, Maciejewski A, Arndt D, Wilson M, Neveu V et al (2014) DrugBank 4.0: shedding new light on drug metabolism. Nucleic Acids Res 42(Database issue):D1091–D1097

3. Leape LL, Bates DW, Cullen DJ, Cooper J, Demonaco HJ, Gallivan T, Hallisey R, Ives J, Laird N, Laffel G et al (1995) Systems analysis of adverse drug events. ADE Prevention Study Group. Jama 274(1):35–43

4. Businaro R (2013) Why we need an efficient and careful pharmacovigilance. J Pharmacovigil 1:4

5. Karbownik A, Szałek E, Sobańska K, Grabowski T, Wolc A, Grześkowiak E (2017) Pharmacokinetic drug–drug interaction between erlotinib and paracetamol: a potential risk for clinical practice. Eur J Pharm Sci 102:55–62

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