Reverse engineering gene regulatory networks with various machine learning methods

Grzegorczyk, M., Husmeier, D. and Werhli, A. (2008) Reverse engineering gene regulatory networks with various machine learning methods. In: Emmert-Streib, F. and Dehmer, M. (eds.) Analysis of Microarray Data: A Network-Based Approach. Wiley-VCH: Weinheim, Germany, pp. 101-142. ISBN 9783527318223

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An important problem in systems biology is the inference of biochemical pathways and regulatory networks from postgenomic data. Various reverse engineering methods have been proposed in the literature, and it is important to understand their relative merits and shortcomings. In the present chapter, we compare the accuracy of reconstructing gene regulatory networks with three di®erent modelling and inference paradigms: (1) Relevance networks (RNs): pairwise association scores independent of the remaining network; (2) graphical Gaussian models (GGMs): undirected graphical models with constraint-based inference, and (3) Bayesian networks (BNs): directed graphical models with score-based inference. After providing a concise and self-contained introduction to the methodological concepts of these three approaches, we present a comparative evaluation using both laboratory data from cytometry experiments as well as simulated data. Our evaluation is based on a published gold-standard cellular regulatory network describing the interaction of eleven phosphorylated proteins and phospholipids in human immune system cells. Of particular interest in our study is a comparison between passive observations and active interventions, and a quanti¯cation of the improvement in network reconstruction accuracy obtained from the latter.

Item Type:Book Sections
Glasgow Author(s) Enlighten ID:Husmeier, Professor Dirk
Authors: Grzegorczyk, M., Husmeier, D., and Werhli, A.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
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