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Title: The local edge machine : inference of dynamic models of gene regulation
Authors: McGoff, KA
Guo, X 
Deckard, A
Kelliher, CM
Leman, AR
Francey, LJ
Hogenesch, JB
Haase, SB
Harer, JL
Keywords: Gene regulatory networks
Time series
Issue Date: 2016
Publisher: BioMed Central
Source: Genome biology, 2016, v. 17, no. 1, 214 How to cite?
Journal: Genome biology 
Abstract: We present a novel approach, the Local Edge Machine, for the inference of regulatory interactions directly from time-series gene expression data. We demonstrate its performance, robustness, and scalability on in silico datasets with varying behaviors, sizes, and degrees of complexity. Moreover, we demonstrate its ability to incorporate biological prior information and make informative predictions on a well-characterized in vivo system using data from budding yeast that have been synchronized in the cell cycle. Finally, we use an atlas of transcription data in a mammalian circadian system to illustrate how the method can be used for discovery in the context of large complex networks.
ISSN: 1474-7596
DOI: 10.1186/s13059-016-1076-z
Rights: © 2016 The Author(s). Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.
The following publication McGoff, K. A., Guo, X., Deckard, A., Kelliher, C. M., Leman, A. R., Francey, L. J., Hogenesch, J. B., Haase, S. B, & Harer, J. L. (2016). The Local Edge Machine: inference of dynamic models of gene regulation. Genome biology, 17(1), 214 is available at
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