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

Journal of Pharmacokinetics and Pharmacodynamics

Volumn 33, Issue 2, 2006, Pages 195-221

  Bies, Robert R ^a   Muldoon, Matthew F ^a   Pollock, Bruce G ^b   Manuck, Steven ^c   Smith, Gwenn ^d   Sale, Mark E ^e  

^a UNIVERSITY OF PITTSBURGH   (United States)

^b UNIVERSITY OF TORONTO   (Canada)

^c UNIVERSITY OF PITTSBURGH SCHOOL OF MEDICINE   (United States)

^d ALBERT EINSTEIN COLLEGE OF MEDICINE   (United States)

^e Next Level Solutions   (United States)

Author keywords

Automated machine learning; Covariate selection; Genetic algorithm; Model building; Nonlinear mixed effects modeling; Population paramacokinetics

Indexed keywords

ANALYTICAL ERROR; ARTICLE; COVARIANCE; GENETIC ALGORITHM; LEARNING; NONLINEAR SYSTEM; PRIORITY JOURNAL; STATISTICAL MODEL;

ALGORITHMS; ANTIDEPRESSIVE AGENTS, SECOND-GENERATION; ARTIFICIAL INTELLIGENCE; CITALOPRAM; COMPUTER SIMULATION; HUMANS; INFUSIONS, INTRAVENOUS; MODELS, BIOLOGICAL; MODELS, GENETIC; SOFTWARE;

EID: 33645806542     PISSN: 1567567X     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10928-006-9004-6     Document Type: Article

References (28)

1. C. José, Pinheiro, D. M. Bates, and M. J. Lindstrom. Model building for nonlinear mixed effects models. Proc. of The Biopharmaceutical Section, Joint Statistical Meetings, 1994.
2. P. O. Maitre, M. Buhrer, D. Thomson, and D. R. Stanski. A three-step approach combining Bayesian regression and NONMEM population analysis: application to midazolam. J. Pharmacokinet. Biopharm. 19(4):377-384 (1991).
3. U. Wahlby, E. N. Jonsson, and M. O. Karlsson. Comparison of stepwise covariate model building strategies in population pharmacokinetic- pharmacodynamic analysis. Aaps Pharmsci. 4(4):E27 (2002).
4. Z. Michalewicz and D. Fogel. How to Solve it: Modern Heuristics SpringerVerlag, New York. 1988.
5. C. H. Papadimitriou and K. Steiglitz. Combinatorial optimization. Algorithms and Complexity. Dover Publications, Mineola, New York, 1998.
6. J. R. Wade, S. L. Beal, and N. C. Sambol. Interaction between structural, statistical, and covariate models in population pharmacokinetic analysis. J. Pharmacokinet. Biopharm. 22(2):165-177 (1994).
7. K. Kowalski and M. Hutmacher. Efficient screening of covariates in population models using Wald's approximation to the likelihood ratio test. J. Pharmacokinet. Pharmacodyn. 28(3):253-275 (2001).
8. G. Givens and J. Hoeting. Computational Statistics. Wiley, New York 2005.
9. D. Goldberg. Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Boston, 1989.
10. L. Chambers. Practical Handbook of Genetic Algorithms - Applications Vol 1. CRC Press, Boca Raton, 1995.
11. R. Bies, M. E. Sale, and B. E. Pollock. Comparing binary tree and automated machine learning covariate search strategies. PAGE meeting, (2003).
12. J. D. Lohn, G. S. Hornby, and D. S. Linden. An Evolved Antenna for Deployment on NASA's Space Technology 5 Mission, 2004 Genetic Programming Theory Practice Workshop (GPTP-2004).
13. T. K. L. Tong and A. P. C. Chan. Genetic algorithm optimization in building portfolio management. Construction Manag. Econ. 19(6):601-609 (2001).
14. J. Shapcott. Index Tracking: Genetic algorithms for investment portfolio selection. Report EPCC-SS92-24, Edinburgh parallel computing centre, The University of Edinburgh, JCMB 1992.
15. I. S. Oh, J. S. Lee, and B. R. Moon. Hybrid genetic algorithms for feature selection. IEEE Trans. Pattern Anal. Mach. Intell. 24(11):1424-1436 (2004).
16. T. A. Cofolga: A Genetic Algorithm for finding the common folding of RNA. Comput. Biol. Chem. (2005).
17. H. N. Lin, K. P. Wu, J. M. Chang, T. Y. Sung, and W. L. Hsu. GANA-a genetic algorithm for NMR backbone resonance assignment. Nucl Acids Res. 33(14):4593-4601 (2005).
18. P. S. Heckerling, B. S. Gerber, T. G. Tape, and R. S. Wigton. Selection of predictor variables for pneumonia using neural networks and genetic algorithms. Methods Inf. Med. 44(1):89-97 (2005).
19. I. S. Oh, J. S. Lee, and B. R. Moon. Hybrid genetic algorithms for feature selection. IEEE Trans. Pattern Anal Mach. Intell. 26(11):1424-1437 (2004).
20. N. G. Best, M. K. Cowles, and S. K. Vines. CODA: Convergence diagnosis and output analysis software for Gibbs sampling output. Version 0.4. MRC Biostatistics Unit, Cambridge University Press, Cambridge; 1997. http://www.mrc-bsu.cam.ac.uk/bugs/classic/coda04/readme.shtml
21. M. D. Hanover, Globomax. Copyright of the University of California, San Francisco.
22. http://www.gnu.org/software/fortran/fortran.html
23. E. N. Jonsson and M. O. Karlsson. Xpose-an S-PLUS based population pharmacokinetic/pharmacodynamic model building aid for NONMEM. Comput. Methods Prog. Biomed. 58(1):51-64 (1999).
24. J. W. Mandema, D. Verotta, and L. B. Sheiner. Building population pharmacokinetic - pharmacodynamic models. I. Models for covariate effects. J. Pharmacokinet. Biopharm. 20(5):511-528 (1992).
25. H. Akaike. Information theory and an extension of the maximum likelihood principle. In Second International Symposium on Information Theory, B. Petrox and F. Caski (eds.)
26. Y. Yano, S. L. Beal, and L. B. Sheiner. Evaluating pharmacokinetic/ pharmacodynamic models using the posterior predictive check. J. Pharmacokint. Pharmacodyn. 28(2):171-192 (2001).
27. L. B. Sheiner, and S. L. Beal. Some suggestions for measuring predictive performance. J. Pharmacokinet. Biopharm. 9(4):503-512 (1981).
28. F. Mentre and S. Escolano. Prediction discrepancies for the evaluation of nonlinear mixed-effects models. J. Pharmacokint. Pharmacodyn. (In press) (2005).