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matlab-based data2dynamics modeling environment  (MathWorks Inc)


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    MathWorks Inc matlab-based data2dynamics modeling environment
    Matlab Based Data2dynamics Modeling Environment, 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/product/matlab-based+modelling+environment+data2dynamics/pm29512437-25-7-6
    Average 90 stars, based on 1 article reviews
    matlab-based data2dynamics modeling environment - by Bioz Stars, 2026-10
    90/100 stars

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    Article Title: Reducing Structural Nonidentifiabilities in Upstream Bioprocess Models Using Profile-Likelihood.
    Article Snippet: The normalized root‐mean squared error was calculated for each observable by: NRMSE y y = − . y t N − y(Θ, ) max min N l D l (7) All calculations were performed in Matlab R2019a and analyses were carried out using the data2dynamics toolbox (Raue et al. 2015) with the lsqnonlin optimizer of Matlab.

    Article Title: Plant cold acclimation and its impact on sensitivity of carbohydrate metabolism
    Article Snippet: The free Matlab software package Data2Dynamics was used for parameter estimation.

    Article Title: Likelihood-ratio test statistic for the finite-sample case in nonlinear ordinary differential equation models.
    Article Snippet: All analyses were conducted using the Matlab-based Data2Dynamics modelling framework [60] on the Baden-Württemberg High Performance Cluster (bwHPC) MLS&WISO, where the analysis was completed within approximately 3 weeks of walltime by using up to 100 16-core 2.4 GHz-nodes in parallel.

    Article Title: Structural and practical identifiability analysis in bioengineering: a beginner’s guide
    Article Snippet: The PIA was conducted in MATLAB using the data2dynamics toolkit [ , ].

    Article Title: An extreme value analysis of water levels at the Akosombo dam, Ghana
    Article Snippet: The identifiability of the final model was tested using the Matlab package Data2Dynamics due to .

    Article Title: New pharmacodynamic parameters linked with ibrutinib responses in chronic lymphocytic leukemia: Prospective study in real-world patients and mathematical modeling
    Article Snippet: Parameter value estimation and data fitting have been performed using Data2Dynamics, a Matlab R2019b add-on that allows to fit ordinary differential equation models to data and imple-ments, among other specificities, statistical assessment of parameter [6, 7].

    Article Title: New pharmacodynamic parameters linked with ibrutinib responses in chronic lymphocytic leukemia: Prospective study in real-world patients and mathematical modeling
    Article Snippet: Parameter value estimation and data fitting have been performed using Data2Dynamics [ , ], a Matlab R2019b add-on , and for each parameter, profile likelihood-based confidence intervals [ ] are provided.

    Article Title: Transcriptional regulators ensuring specific gene expression and decision-making at high TGFβ doses
    Article Snippet: The open-source Matlab package Data2Dynamics (D2D) was used for model fitting ( , ).



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    matlab-based modelling environment 'data2dynamics  (MathWorks Inc)
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    MathWorks Inc matlab-based modelling environment 'data2dynamics
    The parameter θ m ∈ θ was varied over a broad range of values and for each fixed value of θ m , the increase in D P L ( θ m ) = min θ ˜ m - 2 log ( L ( θ ) ) was computed, with L ( θ ) the likelihood function as defined in , and θ ˜ m = { θ 1 , … , θ m - 1 , θ m + 1 , θ N } . The 99% confidence interval threshold is shown as a red dashed line. The parameter values used to generate the synthetic dataset are shown as red dots. The parameter values resulting in the minimal D = min θ - 2 log ( L ( θ ) ) are shown as grey stars. This figure has been generated using the Matlab environment <t>‘Data2Dynamics’</t> [ , ].
    Matlab Based Modelling Environment 'Data2dynamics, 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/product/matlab-based+modelling+environment+data2dynamics/pmc09216621-323-30-27
    Average 90 stars, based on 1 article reviews
    matlab-based modelling environment 'data2dynamics - by Bioz Stars, 2026-10
    90/100 stars
    		  Buy from Supplier
    matlab-based data2dynamics modeling environment  (MathWorks Inc)
    90
    MathWorks Inc matlab-based data2dynamics modeling environment
    The parameter θ m ∈ θ was varied over a broad range of values and for each fixed value of θ m , the increase in D P L ( θ m ) = min θ ˜ m - 2 log ( L ( θ ) ) was computed, with L ( θ ) the likelihood function as defined in , and θ ˜ m = { θ 1 , … , θ m - 1 , θ m + 1 , θ N } . The 99% confidence interval threshold is shown as a red dashed line. The parameter values used to generate the synthetic dataset are shown as red dots. The parameter values resulting in the minimal D = min θ - 2 log ( L ( θ ) ) are shown as grey stars. This figure has been generated using the Matlab environment <t>‘Data2Dynamics’</t> [ , ].
    Matlab Based Data2dynamics Modeling Environment, 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/product/matlab-based+modelling+environment+data2dynamics/pm29512437-25-7-6
    Average 90 stars, based on 1 article reviews
    matlab-based data2dynamics modeling environment - by Bioz Stars, 2026-10
    90/100 stars
    		  Buy from Supplier

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    The parameter θ m ∈ θ was varied over a broad range of values and for each fixed value of θ m , the increase in D P L ( θ m ) = min θ ˜ m - 2 log ( L ( θ ) ) was computed, with L ( θ ) the likelihood function as defined in , and θ ˜ m = { θ 1 , … , θ m - 1 , θ m + 1 , θ N } . The 99% confidence interval threshold is shown as a red dashed line. The parameter values used to generate the synthetic dataset are shown as red dots. The parameter values resulting in the minimal D = min θ - 2 log ( L ( θ ) ) are shown as grey stars. This figure has been generated using the Matlab environment ‘Data2Dynamics’ [ , ].

    Journal: PLoS Computational Biology

    Article Title: A method for the inference of cytokine interaction networks

    doi: 10.1371/journal.pcbi.1010112

    Figure Lengend Snippet: The parameter θ m ∈ θ was varied over a broad range of values and for each fixed value of θ m , the increase in D P L ( θ m ) = min θ ˜ m - 2 log ( L ( θ ) ) was computed, with L ( θ ) the likelihood function as defined in , and θ ˜ m = { θ 1 , … , θ m - 1 , θ m + 1 , θ N } . The 99% confidence interval threshold is shown as a red dashed line. The parameter values used to generate the synthetic dataset are shown as red dots. The parameter values resulting in the minimal D = min θ - 2 log ( L ( θ ) ) are shown as grey stars. This figure has been generated using the Matlab environment ‘Data2Dynamics’ [ , ].

    Article Snippet: Our method depends on the global minimization of a log-likelihood function and a deterministic trust-region approach was used combined with a multi-start strategy, as implemented in the Matlab-based modelling environment ‘Data2Dynamics’ [ ].

    Techniques: Generated

    The parameter θ m = s A ∈ θ was varied over a broad range of values and for each fixed value of θ m , the increase in D P L ( θ m ) = min θ ˜ m - 2 log ( L ( θ ) ) was computed, with L ( θ ) the likelihood function as defined in , and θ ˜ m = { θ 1 , … , θ m - 1 , θ m + 1 , θ N } . The 99% confidence interval threshold is shown as a red dashed line and corresponds to a two order of magnitude interval for s A . The parameter value used to generate the synthetic dataset is shown as a red dot. The parameter value resulting in the minimal D = min θ - 2 log ( L ( θ ) ) is shown as a grey star. This figure has been generated using the Matlab environment ‘Data2Dynamics’ [ , ].

    Journal: PLoS Computational Biology

    Article Title: A method for the inference of cytokine interaction networks

    doi: 10.1371/journal.pcbi.1010112

    Figure Lengend Snippet: The parameter θ m = s A ∈ θ was varied over a broad range of values and for each fixed value of θ m , the increase in D P L ( θ m ) = min θ ˜ m - 2 log ( L ( θ ) ) was computed, with L ( θ ) the likelihood function as defined in , and θ ˜ m = { θ 1 , … , θ m - 1 , θ m + 1 , θ N } . The 99% confidence interval threshold is shown as a red dashed line and corresponds to a two order of magnitude interval for s A . The parameter value used to generate the synthetic dataset is shown as a red dot. The parameter value resulting in the minimal D = min θ - 2 log ( L ( θ ) ) is shown as a grey star. This figure has been generated using the Matlab environment ‘Data2Dynamics’ [ , ].

    Article Snippet: Our method depends on the global minimization of a log-likelihood function and a deterministic trust-region approach was used combined with a multi-start strategy, as implemented in the Matlab-based modelling environment ‘Data2Dynamics’ [ ].

    Techniques: Generated