Review



(mathworks) software suite  (MathWorks Inc)


Bioz Verified Symbol MathWorks Inc is a verified supplier  
  • Logo
  • About
  • News
  • Press Release
  • Team
  • Advisors
  • Partners
  • Contact
  • Bioz Stars
  • Bioz vStars
    • 90
      Buy from Supplier

    Structured Review

    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/result/(mathworks) software suite/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    (mathworks) software suite - by Bioz Stars, 2026-03
    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:



    Similar Products

    (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/result/(mathworks) software suite/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    (mathworks) software suite - by Bioz Stars, 2026-03
    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: