Literature DB >> 16565924

A genetic algorithm-based, hybrid machine learning approach to model selection.

Robert R Bies1, Matthew F Muldoon, Bruce G Pollock, Steven Manuck, Gwenn Smith, Mark E Sale.   

Abstract

We describe a general and robust method for identification of an optimal non-linear mixed effects model. This includes structural, inter-individual random effects, covariate effects and residual error models using machine learning. This method is based on combinatorial optimization using genetic algorithm.

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Year:  2006        PMID： 16565924     DOI： 10.1007/s10928-006-9004-6

Source DB:  PubMed          Journal:  J Pharmacokinet Pharmacodyn        ISSN： 1567-567X            Impact factor:   2.745


  13 in total

1.  Xpose--an S-PLUS based population pharmacokinetic/pharmacodynamic model building aid for NONMEM.

Authors:  E N Jonsson; M O Karlsson
Journal:  Comput Methods Programs Biomed       Date:  1999-01       Impact factor: 5.428

2.  Efficient screening of covariates in population models using Wald's approximation to the likelihood ratio test.

Authors:  K G Kowalski; M M Hutmacher
Journal:  J Pharmacokinet Pharmacodyn       Date:  2001-06       Impact factor: 2.745

3.  Comparison of stepwise covariate model building strategies in population pharmacokinetic-pharmacodynamic analysis.

Authors:  Ulrika Wählby; E Niclas Jonsson; Mats O Karlsson
Journal:  AAPS PharmSci       Date:  2002

4.  Hybrid genetic algorithms for feature selection.

Authors:  Il-Seok Oh; Jin-Seon Lee; Byung-Ro Moon
Journal:  IEEE Trans Pattern Anal Mach Intell       Date:  2004-11       Impact factor: 6.226

5.  Selection of predictor variables for pneumonia using neural networks and genetic algorithms.

Authors:  P S Heckerling; B S Gerber; T G Tape; R S Wigton
Journal:  Methods Inf Med       Date:  2005       Impact factor: 2.176

6.  Cofolga: a genetic algorithm for finding the common folding of two RNAs.

Authors:  Akito Taneda
Journal:  Comput Biol Chem       Date:  2005-04       Impact factor: 2.877

7.  A three-step approach combining Bayesian regression and NONMEM population analysis: application to midazolam.

Authors:  P O Maitre; M Bührer; D Thomson; D R Stanski
Journal:  J Pharmacokinet Biopharm       Date:  1991-08

8.  Some suggestions for measuring predictive performance.

Authors:  L B Sheiner; S L Beal
Journal:  J Pharmacokinet Biopharm       Date:  1981-08

9.  Evaluating pharmacokinetic/pharmacodynamic models using the posterior predictive check.

Authors:  Y Yano; S L Beal; L B Sheiner
Journal:  J Pharmacokinet Pharmacodyn       Date:  2001-04       Impact factor: 2.745

10.  GANA--a genetic algorithm for NMR backbone resonance assignment.

Authors:  Hsin-Nan Lin; Kun-Pin Wu; Jia-Ming Chang; Ting-Yi Sung; Wen-Lian Hsu
Journal:  Nucleic Acids Res       Date:  2005-08-10       Impact factor: 16.971
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  14 in total

Review 1.  A genetic algorithm based global search strategy for population pharmacokinetic/pharmacodynamic model selection.

Authors:  Mark Sale; Eric A Sherer
Journal:  Br J Clin Pharmacol       Date:  2015-01       Impact factor: 4.335

2.  Operating characteristics of stepwise covariate selection in pharmacometric modeling.

Authors:  Malidi Ahamadi; Anna Largajolli; Paul M Diderichsen; Rik de Greef; Thomas Kerbusch; Han Witjes; Akshita Chawla; Casey B Davis; Ferdous Gheyas
Journal:  J Pharmacokinet Pharmacodyn       Date:  2019-04-24       Impact factor: 2.745

3.  Minimax c th percentile of makespan in surgical scheduling.

Authors:  Vikas Agrawal; Aber Elsaleiby; Yue Zhang; P S Sundararaghavan; Andrew Casabianca
Journal:  Health Syst (Basingstoke)       Date:  2019-12-14

Review 4.  Covariate selection in pharmacometric analyses: a review of methods.

Authors:  Matthew M Hutmacher; Kenneth G Kowalski
Journal:  Br J Clin Pharmacol       Date:  2015-01       Impact factor: 4.335

5.  Concordance between criteria for covariate model building.

Authors:  Stefanie Hennig; Mats O Karlsson
Journal:  J Pharmacokinet Pharmacodyn       Date:  2014-03-06       Impact factor: 2.745

6.  Influence of covariate distribution on the predictive performance of pharmacokinetic models in paediatric research.

Authors:  Chiara Piana; Meindert Danhof; Oscar Della Pasqua
Journal:  Br J Clin Pharmacol       Date:  2014-07       Impact factor: 4.335

7.  Application of a single-objective, hybrid genetic algorithm approach to pharmacokinetic model building.

Authors:  Eric A Sherer; Mark E Sale; Bruce G Pollock; Chandra P Belani; Merrill J Egorin; Percy S Ivy; Jeffrey A Lieberman; Stephen B Manuck; Stephen R Marder; Matthew F Muldoon; Howard I Scher; David B Solit; Robert R Bies
Journal:  J Pharmacokinet Pharmacodyn       Date:  2012-07-06       Impact factor: 2.745

8.  The lasso--a novel method for predictive covariate model building in nonlinear mixed effects models.

Authors:  Jakob Ribbing; Joakim Nyberg; Ola Caster; E Niclas Jonsson
Journal:  J Pharmacokinet Pharmacodyn       Date:  2007-05-22       Impact factor: 2.410

9.  Artificial Intelligence and Pharmacometrics: Time to Embrace, Capitalize, and Advance?

Authors:  Ayyappa Chaturvedula; Stacie Calad-Thomson; Chao Liu; Mark Sale; Nandu Gattu; Navin Goyal
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2019-06-05

10.  Genetic Algorithms as a Tool for Dosing Guideline Optimization: Application to Intermittent Infusion Dosing for Vancomycin in Adults.

Authors:  Pieter J Colin; Douglas J Eleveld; Alison H Thomson
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2020-05-19
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