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    Integration of Steady-State and Temporal Gene Expression Data for the Inference of Gene Regulatory Networks

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    11
    Author
    Wang, YK; Hurley, DG; Schnell, S; Print, CG; Crampin, EJ
    Date
    2013-08-14
    Source Title
    PLoS One
    Publisher
    PUBLIC LIBRARY SCIENCE
    University of Melbourne Author/s
    Crampin, Edmund; Hurley, Daniel
    Affiliation
    School of Mathematics and Statistics
    Biomedical Engineering
    Metadata
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    Document Type
    Journal Article
    Citations
    Wang, Y. K., Hurley, D. G., Schnell, S., Print, C. G. & Crampin, E. J. (2013). Integration of Steady-State and Temporal Gene Expression Data for the Inference of Gene Regulatory Networks. PLOS ONE, 8 (8), https://doi.org/10.1371/journal.pone.0072103.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/265513
    DOI
    10.1371/journal.pone.0072103
    Abstract
    We develop a new regression algorithm, cMIKANA, for inference of gene regulatory networks from combinations of steady-state and time-series gene expression data. Using simulated gene expression datasets to assess the accuracy of reconstructing gene regulatory networks, we show that steady-state and time-series data sets can successfully be combined to identify gene regulatory interactions using the new algorithm. Inferring gene networks from combined data sets was found to be advantageous when using noisy measurements collected with either lower sampling rates or a limited number of experimental replicates. We illustrate our method by applying it to a microarray gene expression dataset from human umbilical vein endothelial cells (HUVECs) which combines time series data from treatment with growth factor TNF and steady state data from siRNA knockdown treatments. Our results suggest that the combination of steady-state and time-series datasets may provide better prediction of RNA-to-RNA interactions, and may also reveal biological features that cannot be identified from dynamic or steady state information alone. Finally, we consider the experimental design of genomics experiments for gene regulatory network inference and show that network inference can be improved by incorporating steady-state measurements with time-series data.

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