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Calibrations and validations of biological models with an application on the renal fibrosis
Author(s) -
Karagiannis Georgios,
Hao Wenrui,
Lin Guang
Publication year - 2020
Publication title -
international journal for numerical methods in biomedical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.741
H-Index - 63
eISSN - 2040-7947
pISSN - 2040-7939
DOI - 10.1002/cnm.3329
Subject(s) - calibration , bayesian probability , computer science , gaussian process , field (mathematics) , set (abstract data type) , sensitivity (control systems) , surrogate model , process (computing) , algorithm , artificial intelligence , gaussian , machine learning , mathematics , engineering , statistics , physics , quantum mechanics , electronic engineering , pure mathematics , programming language , operating system
We calibrate a mathematical model of renal tubulointerstitial fibrosis by Hao et al which is used to explore potential drugs for Lupus Nephritis, against a real data set of 84 patients. For this purpose, we present a general calibration procedure which can be used for the calibration analysis of other biological systems as well. Central to the procedure is the idea of designing a Bayesian Gaussian process (GP) emulator that can be used as a surrogate of the fibrosis mathematical model which is computationally expensive to run massively at every input value. The procedure relies on detecting influential model parameters by a GP‐based sensitivity analysis, and calibrating them by specifying a maximum likelihood criterion, tailored to the application, which is optimized via Bayesian global optimization.