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matlab-based toolbox optpbn  (MathWorks Inc)
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MathWorks Inc matlab-based toolbox optpbn
Matlab Based Toolbox Optpbn, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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optpbn toolbox  (SourceForge net)
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SourceForge net optpbn toolbox
Steady-state distributions of output states were generated from the final PBN model using the <t>optPBN</t> <t>toolbox.</t> The mean and standard deviation (SD) of steady-state distribution from ten rounds of simulation (black stars [mean] and error bars [SD] on top) were compared against the experimental data from the training dataset (multi-coloured squares [mean] and error bars [SD] on bottom). Six experimental conditions as labelled on the x-axis are in the following order: DV-WT (WT[-]), DV-WT-Wortmannin (WT[W]), DV-WT-U0126 (WT[U]), DV-dMAPK (dM[-]), DV-dPI3K (dP[-]), and negative control (no doxycycline induction, ND).
Optpbn Toolbox, supplied by SourceForge net, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/optpbn toolbox/product/SourceForge net
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optpbn toolbox - by Bioz Stars, 2026-03
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grid-based version of optpbn toolbox  (SourceForge net)
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SourceForge net grid-based version of optpbn toolbox
Steady-state distributions of output states were generated from the final PBN model using the <t>optPBN</t> <t>toolbox.</t> The mean and standard deviation (SD) of steady-state distribution from ten rounds of simulation (black stars [mean] and error bars [SD] on top) were compared against the experimental data from the training dataset (multi-coloured squares [mean] and error bars [SD] on bottom). Six experimental conditions as labelled on the x-axis are in the following order: DV-WT (WT[-]), DV-WT-Wortmannin (WT[W]), DV-WT-U0126 (WT[U]), DV-dMAPK (dM[-]), DV-dPI3K (dP[-]), and negative control (no doxycycline induction, ND).
Grid Based Version Of Optpbn Toolbox, supplied by SourceForge net, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/grid-based version of optpbn toolbox/product/SourceForge net
Average 90 stars, based on 1 article reviews
grid-based version of optpbn toolbox - by Bioz Stars, 2026-03
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optpbn  (MathWorks Inc)
90
MathWorks Inc optpbn
A preliminary model structure is required as an input for the generation of <t>a</t> <t>PBN</t> model. The generated PBN models from different experimental conditions together with the corresponding measurement data are subsequently combined to generate an integrated optimisation problem which can be solved by various optimisation algorithms. Once the optimisation algorithm(s) generate sufficient amount of good parameter sets, a statistical analysis of the optimised parameter sets (i.e., of PBN's selection probabilities) is performed to indicate the identifiability and the sensitivity of parameters through the consideration on parameters' distribution. The <t>optPBN</t> scripts used for each task are given in parentheses.
Optpbn, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/optpbn/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
optpbn - by Bioz Stars, 2026-03
90/100 stars
  Buy from Supplier

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Steady-state distributions of output states were generated from the final PBN model using the optPBN toolbox. The mean and standard deviation (SD) of steady-state distribution from ten rounds of simulation (black stars [mean] and error bars [SD] on top) were compared against the experimental data from the training dataset (multi-coloured squares [mean] and error bars [SD] on bottom). Six experimental conditions as labelled on the x-axis are in the following order: DV-WT (WT[-]), DV-WT-Wortmannin (WT[W]), DV-WT-U0126 (WT[U]), DV-dMAPK (dM[-]), DV-dPI3K (dP[-]), and negative control (no doxycycline induction, ND).

Journal: PLoS ONE

Article Title: A Probabilistic Boolean Network Approach for the Analysis of Cancer-Specific Signalling: A Case Study of Deregulated PDGF Signalling in GIST

doi: 10.1371/journal.pone.0156223

Figure Lengend Snippet: Steady-state distributions of output states were generated from the final PBN model using the optPBN toolbox. The mean and standard deviation (SD) of steady-state distribution from ten rounds of simulation (black stars [mean] and error bars [SD] on top) were compared against the experimental data from the training dataset (multi-coloured squares [mean] and error bars [SD] on bottom). Six experimental conditions as labelled on the x-axis are in the following order: DV-WT (WT[-]), DV-WT-Wortmannin (WT[W]), DV-WT-U0126 (WT[U]), DV-dMAPK (dM[-]), DV-dPI3K (dP[-]), and negative control (no doxycycline induction, ND).

Article Snippet: This implementation is integrated in the latest version of the optPBN toolbox (version 2.2.3) available on http://sourceforge.net/projects/optpbn .

Techniques: Generated, Standard Deviation, Negative Control

A preliminary model structure is required as an input for the generation of a PBN model. The generated PBN models from different experimental conditions together with the corresponding measurement data are subsequently combined to generate an integrated optimisation problem which can be solved by various optimisation algorithms. Once the optimisation algorithm(s) generate sufficient amount of good parameter sets, a statistical analysis of the optimised parameter sets (i.e., of PBN's selection probabilities) is performed to indicate the identifiability and the sensitivity of parameters through the consideration on parameters' distribution. The optPBN scripts used for each task are given in parentheses.

Journal: PLoS ONE

Article Title: optPBN: An Optimisation Toolbox for Probabilistic Boolean Networks

doi: 10.1371/journal.pone.0098001

Figure Lengend Snippet: A preliminary model structure is required as an input for the generation of a PBN model. The generated PBN models from different experimental conditions together with the corresponding measurement data are subsequently combined to generate an integrated optimisation problem which can be solved by various optimisation algorithms. Once the optimisation algorithm(s) generate sufficient amount of good parameter sets, a statistical analysis of the optimised parameter sets (i.e., of PBN's selection probabilities) is performed to indicate the identifiability and the sensitivity of parameters through the consideration on parameters' distribution. The optPBN scripts used for each task are given in parentheses.

Article Snippet: Based on the existing functionalities of the BN/PBN toolbox , we introduce optPBN , a Matlab-based optimisation toolbox for probabilistic Boolean networks. optPBN allows for a simple generation of PBN models from rule-based Boolean modelling.

Techniques: Generated, Selection