Editor’s note on the themed issue: integration of machine learning and quantitative systems pharmacology
Author:
Publisher
Springer Science and Business Media LLC
Subject
Pharmacology
Link
https://link.springer.com/content/pdf/10.1007/s10928-022-09803-1.pdf
Reference22 articles.
1. Ismail M, Sale M, Yu Y, Liu S, Pflug B, Bies RR (2021) Development of a genetic algorithm and NONMEM workbench for automating and improving population pharmacokinetic/pharmacodynamic model selection. J Pharmacokinet Pharmacodyna (online ahead of print)
2. Sherer EA, Sale ME, Pollock BG, Belani CP, Egorin MJ, Ivy PS, Lieberman JA, Manuck SB, Marder SR, Muldoon MF, Scher HI, Solit DB, Bies RR (2012) Application of a single-objective, hybrid genetic algorithm approach to pharmacokinetic model building. J Pharmacokinet Pharmacodyn 39:393–414
3. Bies RR, Muldoon MF, Pollock BG, Manuck S, Smith G, Sale ME (2006) A genetic algorithm-based, hybrid machine learning approach to model selection. J Pharmacokinet Pharmacodyn 33:195–221
4. Sibieude E, Khandelwal A, Hesthaven JS, Girard P, Terranova N (2021) Fast screening of covariates in population models empowered by machine learning. J Pharmacokinet Pharmacodyn 48:597–609
5. Liu X, Liu C, Huang R, Zhu H, Liu Q, Mitra S, Wang Y (2021) Long short-term memory recurrent neural network for pharmacokinetic-pharmacodynamic modeling. Int J Clin Pharmacol Ther 59:138–146
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