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graphical user interface (gui)-enabled matlab implementation of the model  (MathWorks Inc)


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    MathWorks Inc graphical user interface (gui)-enabled matlab implementation of the model
    Graphical User Interface (Gui) Enabled Matlab Implementation Of The Model, 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+gui+implementation/pmc06864103-78-9-5
    Average 90 stars, based on 1 article reviews
    graphical user interface (gui)-enabled matlab implementation of the model - by Bioz Stars, 2026-11
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    other:

    Article Title: Dataset and multivariate statistical tools for mathematical modeling of water desorption isotherms and infrared spectral properties of cupuassu ( Theobroma grandiflorum L.) pulp
    Article Snippet: Additionally, plot settings, such as axis labels, legends, and titles, can be customized to improve the clarity and informativeness of the visualization. vii) Exporting to MATLAB workspace: The fitted model and associated data, including parameters and fitting statistics, can be exported to the MATLAB workspace for use in other scripts or analyses.

    Article Title: Hyperlaxity and low bone mass predispose young female gymnasts to develop scoliosis suspected status.
    Article Snippet: The model was constructed using the MATLAB software.

    Article Title: A new modified incomprehensible but intelligible-in-time logics algorithm for modeling solid oxide fuel cell.
    Article Snippet: After using the suggested methodology to determine the optimal parameters, this model is built in Simulink/Matlab.

    Article Title: Spatial microfluidic holographic integrated platform for label-free and high-dimensional analysis of cancer heterogeneity.
    Article Snippet: Model trainingrelated information was also exported from MATLAB, including training duration, prediction speed and so on (see Supplementary Note 6 and 7 for details).

    Article Title: Perceptual clustering in auditory streaming
    Article Snippet: The parameters of the model (as well as for the alternative models) were optimised independently for each subject k using the BADS and VBMC toolboxes (as a more robust alternative to MATLAB’s fminsearch routine) [ , , ], to maximise the Evidence Lower Bound (ELBO) based on the likelihood log P m o d e l ( r k | t o n e s , p a r ) combined with a weak prior.

    Article Title: Single particle fluorescence imaging of perovskite nanocrystal crystallization for illustrating coupled nucleation-and-growth
    Article Snippet: A mathematical model of coupled nucleation-and-growth was built and the program was accomplished on Matlab software (Supplementary Note ).

    Article Title: Dataset and machine learning-based computer-aided tools for modeling working sorption isotherms in dried parchment and green coffee beans
    Article Snippet: A dedicated two MATLAB scripts ( SupportVectorMachineLearningModeling_Coffee.m ) and ( RandomForestLearningModeling_Coffee.m) were implemented to perform SVM and RF regression on experimental data, either by fitting individual models for each coffee component (parchment husk, parchment coffee, or green coffee) or by constructing a multivariate model incorporating coffee type as an additional input variable (see DATA DESCRIPTION section).

    Membrane:

    Article Title:
    Article Snippet: .. While the current version of the model requires knowledge of the permeate fugacities to estimate the fluxes of the permeating species, iterative models can be employed that attempt to solve for the fluxes and the permeate compositions simultaneously.11 This approach provides predictive S12 capabilities to the model, although key transport and sorption parameters must still be determined to utilize the model. Model Code (implemented in Matlab): % Notations % D, diffusivity of DMF (1st row) and H2O (2nd row) in membrane, cm2/s % rho_memb, membrane density, g/cm3 % thick_memb, membrane thickness, cm % fugacity, fugacity of DMF (1st row) and H2O (2nd row) upstream (1st col) and downstream (2nd col), kPa % comp_DMF_f, composition of DMF in the feed, wt/wt % sorption_selectivity, ratio of DMF/H2O partition coefficients, DMF/H2O, K(DMF)/K(H2O) % qsat, saturation capacity of DMF (1st row) and H2O (2nd row), mol/g % MolW, molecular weight of DMF (1st row) and H2O (2nd row), g/mol % MolV, molar volume of DMF (1st row) and H2O (2nd row), cm3/mol % rho_liq, density of DMF (1st row) and H2O (2nd row), g/cm3 % delP, transmembrane pressure, bar clc; ..

    Molecular Weight:

    Article Title:
    Article Snippet: .. While the current version of the model requires knowledge of the permeate fugacities to estimate the fluxes of the permeating species, iterative models can be employed that attempt to solve for the fluxes and the permeate compositions simultaneously.11 This approach provides predictive S12 capabilities to the model, although key transport and sorption parameters must still be determined to utilize the model. Model Code (implemented in Matlab): % Notations % D, diffusivity of DMF (1st row) and H2O (2nd row) in membrane, cm2/s % rho_memb, membrane density, g/cm3 % thick_memb, membrane thickness, cm % fugacity, fugacity of DMF (1st row) and H2O (2nd row) upstream (1st col) and downstream (2nd col), kPa % comp_DMF_f, composition of DMF in the feed, wt/wt % sorption_selectivity, ratio of DMF/H2O partition coefficients, DMF/H2O, K(DMF)/K(H2O) % qsat, saturation capacity of DMF (1st row) and H2O (2nd row), mol/g % MolW, molecular weight of DMF (1st row) and H2O (2nd row), g/mol % MolV, molar volume of DMF (1st row) and H2O (2nd row), cm3/mol % rho_liq, density of DMF (1st row) and H2O (2nd row), g/cm3 % delP, transmembrane pressure, bar clc; ..



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    The Graphical User Interface (GUI) for the MATLAB implementation of CAMV. The left tree contains a searchable list of protein hits in order of decreasing MASCOT score, each peptide assignment made to that protein, and all possible combinations of PTMs for each scan. The middle panel contains the MS2 scan data with pre-labeled peaks. On the right is a survey of the precursor window of the MS1 scan and a view of any quantitation information associated with the assignment. The peptide ladder at the top summarizes the sequencing information: red for an identified fragment, black for missing. A peak color-coding scheme allows for rapid surveillance of the quality of the match: within tolerance (green), within 1.5x tolerance (magenta), unmatched peak (red), and isotopic peak (yellow). Peptides which are pre-emptively excluded by the software appear in red on the left-hand tree. For these peptides, the reason for preemptive exclusion is displayed at the top left of the MS2 panel in place of the sequence ladder, along with an option to proceed with processing.

    Journal: Methods (San Diego, Calif.)

    Article Title: Computer Aided Manual Validation of Mass Spectrometry-based Proteomic Data

    doi: 10.1016/j.ymeth.2013.03.004

    Figure Lengend Snippet: The Graphical User Interface (GUI) for the MATLAB implementation of CAMV. The left tree contains a searchable list of protein hits in order of decreasing MASCOT score, each peptide assignment made to that protein, and all possible combinations of PTMs for each scan. The middle panel contains the MS2 scan data with pre-labeled peaks. On the right is a survey of the precursor window of the MS1 scan and a view of any quantitation information associated with the assignment. The peptide ladder at the top summarizes the sequencing information: red for an identified fragment, black for missing. A peak color-coding scheme allows for rapid surveillance of the quality of the match: within tolerance (green), within 1.5x tolerance (magenta), unmatched peak (red), and isotopic peak (yellow). Peptides which are pre-emptively excluded by the software appear in red on the left-hand tree. For these peptides, the reason for preemptive exclusion is displayed at the top left of the MS2 panel in place of the sequence ladder, along with an option to proceed with processing.

    Article Snippet: Once an analysis has been user- verified, publication-ready figures and spreadsheets containing the appropriate quantitative data can be generated from accepted assignments. fig ft0 fig mode=article f1 fig/graphic|fig/alternatives/graphic mode="anchored" m1 Open in a separate window caption a7 The Graphical User Interface (GUI) for the MATLAB implementation of CAMV.

    Techniques: Labeling, Quantitation Assay, Sequencing, Software

    Results of CAMV applied to quantitative tyrosine phosphorylation dataset. (A) All peptide matches following user validation of the dataset. Color code: Agreement between algorithm and user 201/317(green), alternate identification chosen by user 4/317 (yellow), additional assignment included by user 79/317 (blue), and assignment rejected by user 33/317 (red). (B) Distribution of user and CAMV decisions versus MASCOT score. Note that in many cases the user rescued the spectra that were pre-emptively excluded by the software, while in other cases the user rejected the spectral assignment based on the poor quality of the match.

    Journal: Methods (San Diego, Calif.)

    Article Title: Computer Aided Manual Validation of Mass Spectrometry-based Proteomic Data

    doi: 10.1016/j.ymeth.2013.03.004

    Figure Lengend Snippet: Results of CAMV applied to quantitative tyrosine phosphorylation dataset. (A) All peptide matches following user validation of the dataset. Color code: Agreement between algorithm and user 201/317(green), alternate identification chosen by user 4/317 (yellow), additional assignment included by user 79/317 (blue), and assignment rejected by user 33/317 (red). (B) Distribution of user and CAMV decisions versus MASCOT score. Note that in many cases the user rescued the spectra that were pre-emptively excluded by the software, while in other cases the user rejected the spectral assignment based on the poor quality of the match.

    Article Snippet: Once an analysis has been user- verified, publication-ready figures and spreadsheets containing the appropriate quantitative data can be generated from accepted assignments. fig ft0 fig mode=article f1 fig/graphic|fig/alternatives/graphic mode="anchored" m1 Open in a separate window caption a7 The Graphical User Interface (GUI) for the MATLAB implementation of CAMV.

    Techniques: Phospho-proteomics, Biomarker Discovery, Software