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: Treenut Saithong, Somkid Bumee, Chalothorn Liamwirat, Asawin Meechai
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2012-01-01
Series:PLoS ONE
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22272315/pdf/?tool=EBI
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spelling doaj-ca0fb402461e4d4199d1e68d2432a4452021-03-03T20:30:40ZengPublic Library of Science (PLoS)PLoS ONE1932-62032012-01-0171e3023210.1371/journal.pone.0030232Analysis and practical guideline of constraint-based boolean method in genetic network inference.Treenut SaithongSomkid BumeeChalothorn LiamwiratAsawin MeechaiBoolean-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.https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22272315/pdf/?tool=EBI
collection DOAJ
language English
format Article
sources DOAJ
author Treenut Saithong
Somkid Bumee
Chalothorn Liamwirat
Asawin Meechai
spellingShingle Treenut Saithong
Somkid Bumee
Chalothorn Liamwirat
Asawin Meechai
Analysis and practical guideline of constraint-based boolean method in genetic network inference.
PLoS ONE
author_facet Treenut Saithong
Somkid Bumee
Chalothorn Liamwirat
Asawin Meechai
author_sort Treenut Saithong
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.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2012-01-01
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.
url https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22272315/pdf/?tool=EBI
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AT asawinmeechai analysisandpracticalguidelineofconstraintbasedbooleanmethodingeneticnetworkinference
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