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      Gaussian process regression bootstrapping: exploring the effects of uncertainty in time course data

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      Bioinformatics
      Oxford University Press

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          Abstract

          Motivation: Although widely accepted that high-throughput biological data are typically highly noisy, the effects that this uncertainty has upon the conclusions we draw from these data are often overlooked. However, in order to assign any degree of confidence to our conclusions, we must quantify these effects. Bootstrap resampling is one method by which this may be achieved. Here, we present a parametric bootstrapping approach for time-course data, in which Gaussian process regression (GPR) is used to fit a probabilistic model from which replicates may then be drawn. This approach implicitly allows the time dependence of the data to be taken into account, and is applicable to a wide range of problems.

          Results: We apply GPR bootstrapping to two datasets from the literature. In the first example, we show how the approach may be used to investigate the effects of data uncertainty upon the estimation of parameters in an ordinary differential equations (ODE) model of a cell signalling pathway. Although we find that the parameter estimates inferred from the original dataset are relatively robust to data uncertainty, we also identify a distinct second set of estimates. In the second example, we use our method to show that the topology of networks constructed from time-course gene expression data appears to be sensitive to data uncertainty, although there may be individual edges in the network that are robust in light of present data.

          Availability: Matlab code for performing GPR bootstrapping is available from our web site: http://www3.imperial.ac.uk/theoreticalsystemsbiology/data-software/

          Contact: paul.kirk@ 123456imperial.ac.uk , m.stumpf@ 123456imperial.ac.uk

          Supplementary information: Supplementary data are available at Bioinformatics online.

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          Most cited references24

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          An Introduction to the Bootstrap

          Statistics is a subject of many uses and surprisingly few effective practitioners. The traditional road to statistical knowledge is blocked, for most, by a formidable wall of mathematics. The approach in An Introduction to the Bootstrap avoids that wall. It arms scientists and engineers, as well as statisticians, with the computational techniques they need to analyze and understand complicated data sets.
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            A Bayesian Analysis of Some Nonparametric Problems

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              Gaussian processes formachine learning

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                Author and article information

                Journal
                Bioinformatics
                bioinformatics
                bioinfo
                Bioinformatics
                Oxford University Press
                1367-4803
                1460-2059
                15 May 2009
                17 March 2009
                17 March 2009
                : 25
                : 10
                : 1300-1306
                Affiliations
                Centre for Bioinformatics, Division of Molecular Biosciences, Imperial College London, London SW7 2AZ, UK
                Author notes
                *To whom correspondence should be addressed.

                Associate Editor: Limsoon Wong

                Article
                btp139
                10.1093/bioinformatics/btp139
                2677737
                19289448
                f707b38c-c8be-4f8a-8daf-e48a311d8bbe
                © 2009 The Author(s)

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 21 November 2008
                : 3 February 2009
                : 7 March 2009
                Categories
                Original Papers
                Systems Biology

                Bioinformatics & Computational biology
                Bioinformatics & Computational biology

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