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(mathworks) software suite  (MathWorks Inc)


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    MathWorks Inc (mathworks) software suite
    ( A ) MSE of LOOCV as a function of number of the eliminated variables via the backward elimination <t>PLS</t> <t>regression.</t> Coefficient matrix of the full PLS model with 60 input variables ( B ), the best PLS model with 22 input variables ( C ) and the simple PLS model with 5 input variables ( D ). The red and blue colors indicate positive and negative values, respectively. As the number of the variables reduced, the contribution of remained variables relatively increased, and as a result, magnitude of the regression coefficient increased. The scatter plots of the input loadings ( E ), input scores ( F ), output loadings ( G ) and output scores ( H ) of the first and second principal components of the simple PLS model. The colors correspond to the latent variables ( E , G ) and stimuli ( F , H ).
    (Mathworks) Software Suite, 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/(mathworks)+software+suite/pmc03767677-208-3-10
    Average 90 stars, based on 1 article reviews
    (mathworks) software suite - by Bioz Stars, 2026-10
    90/100 stars

    Images

    1) Product Images from "The Extraction of Simple Relationships in Growth Factor-Specific Multiple-Input and Multiple-Output Systems in Cell-Fate Decisions by Backward Elimination PLS Regression"

    Article Title: The Extraction of Simple Relationships in Growth Factor-Specific Multiple-Input and Multiple-Output Systems in Cell-Fate Decisions by Backward Elimination PLS Regression

    Journal: PLoS ONE

    doi: 10.1371/journal.pone.0072780

    ( A ) MSE of LOOCV as a function of number of the eliminated variables via the backward elimination PLS regression. Coefficient matrix of the full PLS model with 60 input variables ( B ), the best PLS model with 22 input variables ( C ) and the simple PLS model with 5 input variables ( D ). The red and blue colors indicate positive and negative values, respectively. As the number of the variables reduced, the contribution of remained variables relatively increased, and as a result, magnitude of the regression coefficient increased. The scatter plots of the input loadings ( E ), input scores ( F ), output loadings ( G ) and output scores ( H ) of the first and second principal components of the simple PLS model. The colors correspond to the latent variables ( E , G ) and stimuli ( F , H ).
    Figure Legend Snippet: ( A ) MSE of LOOCV as a function of number of the eliminated variables via the backward elimination PLS regression. Coefficient matrix of the full PLS model with 60 input variables ( B ), the best PLS model with 22 input variables ( C ) and the simple PLS model with 5 input variables ( D ). The red and blue colors indicate positive and negative values, respectively. As the number of the variables reduced, the contribution of remained variables relatively increased, and as a result, magnitude of the regression coefficient increased. The scatter plots of the input loadings ( E ), input scores ( F ), output loadings ( G ) and output scores ( H ) of the first and second principal components of the simple PLS model. The colors correspond to the latent variables ( E , G ) and stimuli ( F , H ).

    Techniques Used:

    Related Articles

    other:

    Article Title: High resolution multi-parametric probabilistic in vivo atlas of dorsolateral nigral hyperintensity via 7 T MRI
    Article Snippet: QSM maps were computed from the phase of GRE images with a pipeline described in a previous work , using STI Suite (MATLAB toolbox, available at https://people.eecs.berkeley.edu/~chunlei.liu/software.html from UC Berkeley, Berkeley, CA, USA).

    Article Title: Theta-gamma phase-amplitude coupling in psychosis and healthy controls: Predicting working memory capacity across different tasks.
    Article Snippet: Offline processing was done using the MATLAB software suite (MathWorks, Inc., Natick, MA) and the EEGLAB (Delorme and Makeig, 2004) and ERPLAB (Lopez-Calderon and Luck, 2014) packages.

    Article Title: NuclampFISH enables cell sorting based on nuclear RNA expression for chromatin analysis.
    Article Snippet: We segmented and thresholded the images using a custom Matlab software suite [5].

    Article Title: NuclampFISH enables cell sorting based on nuclear RNA expression for chromatin analysis
    Article Snippet: The samples were then mounted for imaging in an antifade buffer containing catalase (Sigma) and glucose oxidase (Sigma) to prevent photobleaching.

    Article Title: Quantitative susceptibility mapping study of deep gray matter iron content in glioma patients
    Article Snippet: All steps were performed in MATLAB (R2013b, MathWorks, Natick, MA, USA) using the STI Suite toolbox ( https://people.eecs.berkeley.edu/~chunlei.liu/software.html ) ( , ).

    Article Title: Serial changes and optimal imaging windows in vessel wall MRI for unruptured intracranial artery dissection
    Article Snippet: QSM images were reconstructed using STI Suite v3 ( https://people.eecs.berkeley.edu/~chunlei.liu/software.html ) in MATLAB R2016a (The Mathworks) , .

    Article Title: High resolution multi-parametric probabilistic in vivo atlas of dorsolateral nigral hyperintensity via 7 T MRI.
    Article Snippet: QSM maps were computed from the phase of GRE images with a pipeline described in a previous work21, using STI Suite (MATLAB toolbox, available at https://people.eecs.berkeley.edu/~chunlei.liu/software.html from UC Berkeley, Berkeley, CA, USA).

    Software:

    Article Title: Enhancing RBP4 protein detection in clinical urine samples with solid-state nanopores through optimized sandwich immunoassay techniques.
    Article Snippet: Nanopore technology is a promising single-molecule sensing platform that can identify substances through the precise monitoring of changes in ion currents.. However, protein detection in clinical samples using solid-state nanopores remains challenging due to their heterogeneously charged spherical structure, which results in signals with extremely low signal-to-noise ratios (SNR) and low capture rates that are difficult to analyze.. In this study, we employed a double-antibody sandwich technique to specifically capture and amplify the target antigen, which significantly improves the SNR and effectively distinguishes the target signal from background interference.



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    (mathworks) software suite  (MathWorks Inc)
    90
    MathWorks Inc (mathworks) software suite
    ( A ) MSE of LOOCV as a function of number of the eliminated variables via the backward elimination <t>PLS</t> <t>regression.</t> Coefficient matrix of the full PLS model with 60 input variables ( B ), the best PLS model with 22 input variables ( C ) and the simple PLS model with 5 input variables ( D ). The red and blue colors indicate positive and negative values, respectively. As the number of the variables reduced, the contribution of remained variables relatively increased, and as a result, magnitude of the regression coefficient increased. The scatter plots of the input loadings ( E ), input scores ( F ), output loadings ( G ) and output scores ( H ) of the first and second principal components of the simple PLS model. The colors correspond to the latent variables ( E , G ) and stimuli ( F , H ).
    (Mathworks) Software Suite, 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/(mathworks)+software+suite/pmc03767677-208-3-10
    Average 90 stars, based on 1 article reviews
    (mathworks) software suite - by Bioz Stars, 2026-10
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    ( A ) MSE of LOOCV as a function of number of the eliminated variables via the backward elimination PLS regression. Coefficient matrix of the full PLS model with 60 input variables ( B ), the best PLS model with 22 input variables ( C ) and the simple PLS model with 5 input variables ( D ). The red and blue colors indicate positive and negative values, respectively. As the number of the variables reduced, the contribution of remained variables relatively increased, and as a result, magnitude of the regression coefficient increased. The scatter plots of the input loadings ( E ), input scores ( F ), output loadings ( G ) and output scores ( H ) of the first and second principal components of the simple PLS model. The colors correspond to the latent variables ( E , G ) and stimuli ( F , H ).

    Journal: PLoS ONE

    Article Title: The Extraction of Simple Relationships in Growth Factor-Specific Multiple-Input and Multiple-Output Systems in Cell-Fate Decisions by Backward Elimination PLS Regression

    doi: 10.1371/journal.pone.0072780

    Figure Lengend Snippet: ( A ) MSE of LOOCV as a function of number of the eliminated variables via the backward elimination PLS regression. Coefficient matrix of the full PLS model with 60 input variables ( B ), the best PLS model with 22 input variables ( C ) and the simple PLS model with 5 input variables ( D ). The red and blue colors indicate positive and negative values, respectively. As the number of the variables reduced, the contribution of remained variables relatively increased, and as a result, magnitude of the regression coefficient increased. The scatter plots of the input loadings ( E ), input scores ( F ), output loadings ( G ) and output scores ( H ) of the first and second principal components of the simple PLS model. The colors correspond to the latent variables ( E , G ) and stimuli ( F , H ).

    Article Snippet: In this study, PLS regression analysis was performed using the MATLAB (Mathworks) software suite.

    Techniques: