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Learning pharmacokinetic models for in vivo glucocorticoid activation

Abstract:

To understand trends in individual responses to medication, one can take a purely data-driven machine learning approach, or alternatively apply pharmacokinetics combined with mixed-effects statistical modelling. To take advantage of the predictive power of machine learning and the explanatory power of pharmacokinetics, we propose a latent variable mixture model for learning clusters of pharmacokinetic models demonstrated on a clinical data set investigating 11β-hydroxysteroid dehydrogenase en...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Publisher's Version

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Publisher copy:
10.1016/j.jtbi.2018.07.025

Authors


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Role:
Author
ORCID:
0000-0002-2930-6172
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Role:
Author
ORCID:
0000-0002-3427-0936
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Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
RDM
Subgroup:
RDM Strategic
Oxford college:
Keble College
Role:
Author
ORCID:
0000-0002-3170-8533
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Grant:
Horizon H2020-EU.1.3.2
Publisher:
Elsevier Publisher's website
Journal:
Journal of Theoretical Biology Journal website
Volume:
455
Pages:
222-231
Publication date:
2018-07-23
Acceptance date:
2018-07-21
DOI:
EISSN:
1095-8541
ISSN:
0022-5193
Pubs id:
pubs:891514
URN:
uri:152682be-23a6-4f0d-95b9-0c8fbcfc335e
UUID:
uuid:152682be-23a6-4f0d-95b9-0c8fbcfc335e
Local pid:
pubs:891514

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