Robust experimental design and feature selection in signal transduction pathway modeling

Fei He, Martin Brown, Hong Yue, Lam Fat Yeung

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

2 Citations (Scopus)

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 "robust" 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 IκB-NF-κB pathway feature selection problem.

Original languageEnglish
Title of host publication2008 International Joint Conference on Neural Networks, IJCNN 2008
Pages1544-1551
Number of pages8
DOIs
Publication statusPublished - 24 Nov 2008
Externally publishedYes
Event2008 International Joint Conference on Neural Networks, IJCNN 2008 - Hong Kong, China
Duration: 1 Jun 20088 Jun 2008

Conference

Conference2008 International Joint Conference on Neural Networks, IJCNN 2008
CountryChina
CityHong Kong
Period1/06/088/06/08

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ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Cite this

He, F., Brown, M., Yue, H., & Yeung, L. F. (2008). Robust experimental design and feature selection in signal transduction pathway modeling. In 2008 International Joint Conference on Neural Networks, IJCNN 2008 (pp. 1544-1551). [4634001] https://doi.org/10.1109/IJCNN.2008.4634001