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Dony, L. ; He, F.* ; Stumpf, M.P.H.*

Parametric and non-parametric gradient matching for network inference: A comparison.

BMC Bioinformatics 20:52 (2019)
Publ. Version/Full Text DOI
Open Access Gold
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BackgroundReverse engineering of gene regulatory networks from time series gene-expression data is a challenging problem, not only because of the vast sets of candidate interactions but also due to the stochastic nature of gene expression. We limit our analysis to nonlinear differential equation based inference methods. In order to avoid the computational cost of large-scale simulations, a two-step Gaussian process interpolation based gradient matching approach has been proposed to solve differential equations approximately.ResultsWe apply a gradient matching inference approach to a large number of candidate models, including parametric differential equations or their corresponding non-parametric representations, we evaluate the network inference performance under various settings for different inference objectives. We use model averaging, based on the Bayesian Information Criterion (BIC), to combine the different inferences. The performance of different inference approaches is evaluated using area under the precision-recall curves.ConclusionsWe found that parametric methods can provide comparable, and often improved inference compared to non-parametric methods; the latter, however, require no kinetic information and are computationally more efficient.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Systems Biology ; Gradient Matching ; Gene Regulation ; Network Inference; Regulatory Networks; Systems
ISSN (print) / ISBN 1471-2105
e-ISSN 1471-2105
Journal BMC Bioinformatics
Quellenangaben Volume: 20, Issue: 1, Pages: , Article Number: 52 Supplement: ,
Publisher BioMed Central
Publishing Place Campus, 4 Crinan St, London N1 9xw, England
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)