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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+data2dynamics+modeling+environment/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

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    1) Product Images from "A method for the inference of cytokine interaction networks"

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

    Journal: PLoS Computational Biology

    doi: 10.1371/journal.pcbi.1010112

    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’ [ , ].
    Figure Legend 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’ [ , ].

    Techniques Used: 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’ [ , ].
    Figure Legend 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’ [ , ].

    Techniques Used: Generated

    Related Articles

    other:

    Article Title: Profile likelihood-based analyses of infectious disease models.
    Article Snippet: Ordinary differential equation models are frequently applied to describe the temporal evolution of epidemics.. However, ordinary differential equation models are also utilized in other scientific fields.. We summarize and transfer state-of-the art approaches from other fields like Systems Biology to infectious disease models.



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    matlab-based modelling environment 'data2dynamics  (MathWorks Inc)
    90
    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+data2dynamics+modeling+environment/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+data2dynamics+modeling+environment/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

    Image Search Results


    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