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Statistical Applications in Genetics and Molecular Biology

Editor-in-Chief: Stumpf, Michael P.H.

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Volume 14, Issue 3 (Jun 2015)

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CSI: a nonparametric Bayesian approach to network inference from multiple perturbed time series gene expression data

Christopher A. Penfold / Ahmed Shifaz / Paul E. Brown / Ann Nicholson / David L. Wild
Published Online: 2015-05-30 | DOI: https://doi.org/10.1515/sagmb-2014-0082

Abstract

Here we introduce the causal structure identification (CSI) package, a Gaussian process based approach to inferring gene regulatory networks (GRNs) from multiple time series data. The standard CSI approach infers a single GRN via joint learning from multiple time series datasets; the hierarchical approach (HCSI) infers a separate GRN for each dataset, albeit with the networks constrained to favor similar structures, allowing for the identification of context specific networks. The software is implemented in MATLAB and includes a graphical user interface (GUI) for user friendly inference. Finally the GUI can be connected to high performance computer clusters to facilitate analysis of large genomic datasets.

Keywords: Bayesian; Gaussian process; gene regulatory networks

References

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About the article

Corresponding author: David L. Wild, Systems Biology Centre, University of Warwick, Coventry, UK, CV4 7AL, e-mail:


Published Online: 2015-05-30

Published in Print: 2015-06-01


Citation Information: Statistical Applications in Genetics and Molecular Biology, ISSN (Online) 1544-6115, ISSN (Print) 2194-6302, DOI: https://doi.org/10.1515/sagmb-2014-0082.

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