pyDarwin : A Machine Learning Enhanced Automated Nonlinear Mixed-Effect Model Selection Toolbox
© 2023 The Authors. Clinical Pharmacology & Therapeutics published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics..
pyDarwin is an open-source Python package for nonlinear mixed-effect model selection. pyDarwin combines machine-learning algorithms and NONMEM to perform a global search for the optimal model in a user-defined model search space. Compared with traditional stepwise search, pyDarwin provides an efficient platform for conducting an objective, robust, less labor-intensive model selection process without compromising model interpretability. In this tutorial, we will begin by introducing the essential components and concepts within the package. Subsequently, we will provide an overview of the pyDarwin modeling workflow and the necessary files needed for model selection. To illustrate the entire process, we will conclude with an example utilizing quetiapine clinical data.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:115 |
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| Enthalten in: |
Clinical pharmacology and therapeutics - 115(2024), 4 vom: 30. Apr., Seite 758-773 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Li, Xinnong [VerfasserIn] |
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| Anmerkungen: |
Date Completed 21.03.2024 Date Revised 11.04.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1002/cpt.3114 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM365285870 |
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| 520 | |a pyDarwin is an open-source Python package for nonlinear mixed-effect model selection. pyDarwin combines machine-learning algorithms and NONMEM to perform a global search for the optimal model in a user-defined model search space. Compared with traditional stepwise search, pyDarwin provides an efficient platform for conducting an objective, robust, less labor-intensive model selection process without compromising model interpretability. In this tutorial, we will begin by introducing the essential components and concepts within the package. Subsequently, we will provide an overview of the pyDarwin modeling workflow and the necessary files needed for model selection. To illustrate the entire process, we will conclude with an example utilizing quetiapine clinical data | ||
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| 700 | 1 | |a Bigos, Kristin L |e verfasserin |4 aut | |
| 700 | 1 | |a Craig, James |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Fenggong |e verfasserin |4 aut | |
| 700 | 1 | |a Feng, Kairui |e verfasserin |4 aut | |
| 700 | 1 | |a Hu, Meng |e verfasserin |4 aut | |
| 700 | 1 | |a Bies, Robert |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, Liang |e verfasserin |4 aut | |
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