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matlab mathworks© suite  (MathWorks Inc)


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    MathWorks Inc matlab mathworks© suite
    Matlab Mathworks© 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%C2%AE+suite/bio_rxiv__2024__07__15__603621-177-16-17
    Average 90 stars, based on 1 article reviews
    matlab mathworks© suite - by Bioz Stars, 2026-09
    90/100 stars

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    other:

    Article Title: An integrated in silico - in vitro approach for identifying therapeutic targets against osteoarthritis
    Article Snippet: All implementations and simulations were carried with the MathWorks® suite, MATLAB (2018b).



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    ( 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 ).
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    ( 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 ).
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    ( 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: