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Robust experimental design and feature selection in signal transduction pathway modeling

He, F. and Brown, M. and Yue, H. and Yeung, L.F. (2008) Robust experimental design and feature selection in signal transduction pathway modeling. In: IEEE International Joint Conference on Neural Networks, 2008. IJCNN 2008. IEEE, Hong Kong, China, pp. 1544-1551. ISBN 978-1-4244-1820-6

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Abstract

Due to the general lack of experimental data for biochemical pathway model identification, cell-level time series experimental design is particularly important in current systems biology research. This paper investigates the problem of experimental design for signal transduction pathway modeling, and in particular, focuses on methods for parametric feature selection. An important problem is the estimation of parametric uncertainty which is a function of the true (but unknown) parameters. In this paper, two ldquorobustrdquo feature selection strategies are investigated The first is a mini-max robust experimental design approach, the second is a sampled experimental design method inspired by the Morris global sensitivity analysis. The two approaches are analyzed and interpreted in terms of a generalized optimal experimental design criterion, and their performance has been compared via simulation on the IkappaB-NF-kappaB pathway feature selection problem.

Item type: Book Section
ID code: 12808
Keywords: biochemistry, design of experiments, feature extraction, sensitivity analysis, Morris global sensitivity analysis, biochemical pathway model identification, feature selection, robust experimental design, signal transduction pathway modeling, Electrical engineering. Electronics Nuclear engineering
Subjects: Technology > Electrical engineering. Electronics Nuclear engineering
Department: Faculty of Engineering > Electronic and Electrical Engineering
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Depositing user: Strathprints Administrator
Date Deposited: 18 Oct 2010 11:25
Last modified: 04 Feb 2013 16:34
URI: http://strathprints.strath.ac.uk/id/eprint/12808

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