Assessment of regression methods for inference of regulatory networks involved in circadian regulation

Aderhold, A., Husmeier, D. , Smith, V.A., Millar, A.J. and Grzegorczyk, M. (2013) Assessment of regression methods for inference of regulatory networks involved in circadian regulation. In: Proceedings of the 10th International Workshop on Computational Systems Biology. Tampere International Center for Signal Processing: Tampere, Finland, pp. 29-33. ISBN 9789521530913

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We assess the accuracy of three established regression methods for reconstructing gene and protein regulatory networks in the context of circadian regulation. Data are simulated from a recently published regulatory network of the circadian clock in Arabidopsis thaliana, in which protein and gene interactions are described by a Markov jump process based on Michaelis-Menten kinetics. We closely follow recent experimental protocols, including the entrainment of seedlings to different light-dark cycles and the knock-out of various key regulatory genes. Our study provides relative assessment scores for the comparison of state-of-the art regression methods, investigates the influence of systematically missing values related to unknown protein concentrations and mRNA transcription rates, and quantifies the dependence of the performance on the degree of recurrency.

Item Type:Book Sections
Glasgow Author(s) Enlighten ID:Husmeier, Professor Dirk
Authors: Aderhold, A., Husmeier, D., Smith, V.A., Millar, A.J., and Grzegorczyk, M.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Publisher:Tampere International Center for Signal Processing
Copyright Holders:Copyright © 2013 The Authors
Publisher Policy:Reproduced with the permission of the publisher

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