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Predicting simulation parameters of biological systems using a Gaussian process model
Author(s) -
Zhu Xiangxin,
Welling Max,
Jin Fang,
Lowengrub John
Publication year - 2012
Publication title -
statistical analysis and data mining: the asa data science journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.381
H-Index - 33
eISSN - 1932-1872
pISSN - 1932-1864
DOI - 10.1002/sam.11163
Subject(s) - computer science , gaussian process , flexibility (engineering) , parametric statistics , kriging , regression , gaussian , algorithm , mathematical optimization , machine learning , mathematics , statistics , physics , quantum mechanics
Finding optimal parameters for simulating biological systems is usually a very difficult and expensive task in systems biology. Brute force searching is infeasible in practice because of the huge (often infinite) search space. In this article, we propose predicting the parameters efficiently by learning the relationship between system outputs and parameters using regression. However, the conventional parametric regression models suffer from two issues, thus are not applicable to this problem. First, restricting the regression function as a certain fixed type (e.g. linear, polynomial, etc.) introduces too strong assumptions that reduce the model flexibility. Second, conventional regression models fail to take into account the fact that a fixed parameter value may correspond to multiple different outputs due to the stochastic nature of most biological simulations, and the existence of a potentially large number of other factors that affect the simulation outputs. We propose a novel approach based on a Gaussian process model that addresses the two issues jointly. We apply our approach to a tumor vessel growth model and the feedback Wright–Fisher model. The experimental results show that our method can predict the parameter values of both of the two models with high accuracy. © 2012 Wiley Periodicals, Inc. Statistical Analysis and Data Mining, 2012