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Learning pharmacokinetic models for in vivo glucocorticoid activation.

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第一作者： Kerstin,Bunte

第一单位： School of Computer Science, The University of Birmingham, Birmingham B15 2TT, UK; Faculty of Science and Engineering, University of Groningen, P.O. Box 407, Groningen 9700 AK, Netherlands. Electronic address: kerstin.bunte@googlemail.com.

作者： Kerstin,Bunte ^[1] ; David J,Smith ^[2] ; Michael J,Chappell ^[3] ; Zaki K,Hassan-Smith ^[4] ; Jeremy W,Tomlinson ^[5] ; Wiebke,Arlt ^[6] ; Peter,Tiňo ^[7]

作者单位： School of Computer Science, The University of Birmingham, Birmingham B15 2TT, UK; Faculty of Science and Engineering, University of Groningen, P.O. Box 407, Groningen 9700 AK, Netherlands. Electronic address: kerstin.bunte@googlemail.com. ^[1] School of Mathematics, The University of Birmingham, Birmingham B15 2TT, UK; Institute of Metabolism and Systems Research, University of Birmingham, UK. ^[2] School of Engineering, University of Warwick, Coventry CV4 7AL, UK. ^[3] Centre for Applied Biological and Exercise Science, Coventry University, Coventry, UK; Departments of Endocrinology and Acute Internal Medicine, Queen Elizabeth Hospital Birmingham, Birmingham B15 2TH, UK; Centre of Endocrinology, Diabetes and Metabolism, Queen Elizabeth Hospital Birmingham, Birmingham Health Partners, UK. ^[4] Oxford Centre for Diabetes, Endocrinology and Metabolism, NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK. ^[5] Institute of Metabolism and Systems Research, University of Birmingham, UK; Centre of Endocrinology, Diabetes and Metabolism, Queen Elizabeth Hospital Birmingham, Birmingham Health Partners, UK. ^[6] School of Computer Science, The University of Birmingham, Birmingham B15 2TT, UK; Institute of Metabolism and Systems Research, University of Birmingham, UK. ^[7]

关键词 11β-HSD activity Clustering Dynamic systems Expectation maximization Gaussian mixture model Identifiability analysis In vivo glucocorticoid activation Partially observed time series analysis Perturbation analysis Pharmacokinetics Probabilistic models