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Computational Modelling Strategies for Gene Regulatory Network Reconstruction

Sehgal, Shoaib; Gondal, Iqbal and Dooley, Laurence S. (2008). Computational Modelling Strategies for Gene Regulatory Network Reconstruction. In: Kelemen, Arpad; Abraham, Ajith and Liang, Yulan eds. Computational Intelligence in Medical Informatics. Studies in Computational Intelligence, 85. Berlin: Springer-Verlag, pp. 207–220.

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Gene Regulatory Network (GRN) modelling infers genetic interactions between different genes and other cellular components to elucidate the cellular functionality. This GRN modelling has overwhelming applications in biology starting from diagnosis through to drug target identification. Several GRN modelling methods have been proposed in the literature, and it is important to study the relative merits and demerits of each method. This chapter provides a comprehensive comparative study on GRN reconstruction algorithms. The methods discussed in this chapter are diverse and vary from simple similarity based methods to state of the art hybrid and probabilistic methods. In addition, the chapter also underpins the need of strategies which should be able to model the stochastic behavior of gene regulation in the presence of limited number of samples, noisy data, multi-collinearity for high number of genes.

Item Type: Book Section
ISBN: 3-540-75766-X, 978-3-540-75766-5
Keywords: Gene Regulatory Networks; Deterministic Modelling; Stochastic Modelling; Computational Intelligence Methods for GRN Modelling;
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Research Group: Centre for Research in Computing (CRC)
Item ID: 10505
Depositing User: Laurence Dooley
Date Deposited: 08 Apr 2008
Last Modified: 07 Dec 2018 09:09
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