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dc.contributor.authorBerestovsky, Natalie
Nakhleh, Luay
dc.date.accessioned 2013-07-12T19:38:31Z
dc.date.available 2013-07-12T19:38:31Z
dc.date.issued 2013
dc.identifier.citation Berestovsky, Natalie and Nakhleh, Luay. "An Evaluation of Methods for Inferring Boolean Networks from Time-Series Data." PLoS One, 8, no. 6 (2013) Public Library of Science: e66031. https://doi.org/10.1371/journal.pone.0066031.
dc.identifier.urihttps://hdl.handle.net/1911/71544
dc.description.abstractRegulatory networks play a central role in cellular behavior and decision making. Learning these regulatory networks is a major task in biology, and devising computational methods and mathematical models for this task is a major endeavor in bioinformatics. Boolean networks have been used extensively for modeling regulatory networks. In this model, the state of each gene can be either ‘on’ or ‘off’ and that next-state of a gene is updated, synchronously or asynchronously, according to a Boolean rule that is applied to the current-state of the entire system. Inferring a Boolean network from a set of experimental data entails two main steps: first, the experimental time-series data are discretized into Boolean trajectories, and then, a Boolean network is learned from these Boolean trajectories. In this paper, we consider three methods for data discretization, including a new one we propose, and three methods for learning Boolean networks, and study the performance of all possible nine combinations on four regulatory systems of varying dynamics complexities. We find that employing the right combination of methods for data discretization and network learning results in Boolean networks that capture the dynamics well and provide predictive power. Our findings are in contrast to a recent survey that placed Boolean networks on the low end of the ‘‘faithfulness to biological reality’’ and ‘‘ability to model dynamics’’ spectra. Further, contrary to the common argument in favor of Boolean networks, we find that a relatively large number of time points in the timeseries data is required to learn good Boolean networks for certain data sets. Last but not least, while methods have been proposed for inferring Boolean networks, as discussed above, missing still are publicly available implementations thereof. Here, we make our implementation of the methods available publicly in open source at http://bioinfo.cs.rice.edu/.
dc.language.iso eng
dc.publisher Public Library of Science
dc.rights This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.title An Evaluation of Methods for Inferring Boolean Networks from Time-Series Data
dc.type Journal article
dc.contributor.funder Alfred P. Sloan Foundation
dc.contributor.funder John and Ann Doerr Fund for Computational Biomedicine
dc.citation.journalTitle PLoS One
dc.citation.volumeNumber 8
dc.citation.issueNumber 6
dc.embargo.terms none
dc.type.dcmi Text
dc.identifier.doihttps://doi.org/10.1371/journal.pone.0066031
dc.identifier.pmcid PMC3689729
dc.identifier.pmid 23805196
dc.identifier.grantID Seed Grant- Gulf Coast Center for Computational Cancer Research (John and Ann Doerr Fund for Computational Biomedicine)
dc.identifier.grantID Research Fellowship (Alfred P. Sloan Foundation)
dc.type.publication publisher version
dc.citation.firstpage e66031


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This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Except where otherwise noted, this item's license is described as This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.