Analysis and Practical Guideline of Constraint-Based Boolean Method in Genetic Network Inference

Boolean-based method, despite of its simplicity, would be a more attractive approach for inferring a network from high-throughput expression data if its effectiveness has not been limited by high false positive prediction. In this study, we explored factors that could simply be adjusted to improve t...

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Main Authors:	Saithong, Treenut, Bumee, Somkid, Liamwirat, Chalothorn, Meechai, Asawin
Format:	Online
Language:	English
Published:	Public Library of Science 2012
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3260258/

id	pubmed-3260258
recordtype	oai_dc
spelling	pubmed-32602582012-01-23 Analysis and Practical Guideline of Constraint-Based Boolean Method in Genetic Network Inference Saithong, Treenut Bumee, Somkid Liamwirat, Chalothorn Meechai, Asawin Research Article Boolean-based method, despite of its simplicity, would be a more attractive approach for inferring a network from high-throughput expression data if its effectiveness has not been limited by high false positive prediction. In this study, we explored factors that could simply be adjusted to improve the accuracy of inferring networks. Our work focused on the analysis of the effects of discretisation methods, biological constraints, and stringency of Boolean function assignment on the performance of Boolean network, including accuracy, precision, specificity and sensitivity, using three sets of microarray time-series data. The study showed that biological constraints have pivotal influence on the network performance over the other factors. It can reduce the variation in network performance resulting from the arbitrary selection of discretisation methods and stringency settings. We also presented the master Boolean network as an approach to establish the unique solution for Boolean analysis. The information acquired from the analysis was summarised and deployed as a general guideline for an efficient use of Boolean-based method in the network inference. In the end, we provided an example of the use of such a guideline in the study of Arabidopsis circadian clock genetic network from which much interesting biological information can be inferred. Public Library of Science 2012-01-17 /pmc/articles/PMC3260258/ /pubmed/22272315 http://dx.doi.org/10.1371/journal.pone.0030232 Text en Saithong et al. http://creativecommons.org/licenses/by/4.0/ 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 properly credited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Saithong, Treenut Bumee, Somkid Liamwirat, Chalothorn Meechai, Asawin
spellingShingle	Saithong, Treenut Bumee, Somkid Liamwirat, Chalothorn Meechai, Asawin Analysis and Practical Guideline of Constraint-Based Boolean Method in Genetic Network Inference
author_facet	Saithong, Treenut Bumee, Somkid Liamwirat, Chalothorn Meechai, Asawin
author_sort	Saithong, Treenut
title	Analysis and Practical Guideline of Constraint-Based Boolean Method in Genetic Network Inference
title_short	Analysis and Practical Guideline of Constraint-Based Boolean Method in Genetic Network Inference
title_full	Analysis and Practical Guideline of Constraint-Based Boolean Method in Genetic Network Inference
title_fullStr	Analysis and Practical Guideline of Constraint-Based Boolean Method in Genetic Network Inference
title_full_unstemmed	Analysis and Practical Guideline of Constraint-Based Boolean Method in Genetic Network Inference
title_sort	analysis and practical guideline of constraint-based boolean method in genetic network inference
description	Boolean-based method, despite of its simplicity, would be a more attractive approach for inferring a network from high-throughput expression data if its effectiveness has not been limited by high false positive prediction. In this study, we explored factors that could simply be adjusted to improve the accuracy of inferring networks. Our work focused on the analysis of the effects of discretisation methods, biological constraints, and stringency of Boolean function assignment on the performance of Boolean network, including accuracy, precision, specificity and sensitivity, using three sets of microarray time-series data. The study showed that biological constraints have pivotal influence on the network performance over the other factors. It can reduce the variation in network performance resulting from the arbitrary selection of discretisation methods and stringency settings. We also presented the master Boolean network as an approach to establish the unique solution for Boolean analysis. The information acquired from the analysis was summarised and deployed as a general guideline for an efficient use of Boolean-based method in the network inference. In the end, we provided an example of the use of such a guideline in the study of Arabidopsis circadian clock genetic network from which much interesting biological information can be inferred.
publisher	Public Library of Science
publishDate	2012
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3260258/
_version_	1611500475199258624

Analysis and Practical Guideline of Constraint-Based Boolean Method in Genetic Network Inference

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