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libsvm node  (KNIME GmbH)

 
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    KNIME GmbH libsvm node
    Libsvm Node, supplied by KNIME GmbH, 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/libsvm+node/libsvm/pmc08982525-146-13-10
    Average 90 stars, based on 1 article reviews
    libsvm node - by Bioz Stars, 2026-11
    90/100 stars

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    Related Articles

    other:

    Article Title: Docking-generated multiple ligand poses for bootstrapping bioactivity classifying Machine Learning: Repurposing covalent inhibitors for COVID-19-related TMPRSS2 as case study
    Article Snippet: The following default settings were used in both SVM methods as implemented in the WEKA-KNIME (version 4.1.3) LibSVM node, these include: Kernel Cache (Cache Size = 40.0), kernel type is radial basis function: exp(-gamma*|u-v|^2), and loss function is 0.1, kernel coefficients epsilon = 0.001 and Gamma = 0.00.

    Article Title: Discovery of new Cdc2-like kinase 4 (CLK4) inhibitors via pharmacophore exploration combined with flexible docking-based ligand/receptor contact fingerprints and machine learning
    Article Snippet: Other parameters were set to their default values in the Weka-Knime (version 4.1.3) LibSVM node, these include: kernel cache (cache size = 40.0), kernel coefficients epsilon = 0.001 and gamma = 0.00, kernel type is radial basis function: exp(−gamma × | u − v |^2), and loss function is 0.1.



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    Image Search Results


    Accuracy, cohen’s κ, TPR and TNR values based for the all ML tools and using either 5-fold cross validation or 20% testing set for prediction people with T2DM from people without T2DM.

    Journal: PLoS ONE

    Article Title: Investigating the association of CD36 gene polymorphisms (rs1761667 and rs1527483) with T2DM and dyslipidemia: Statistical analysis, machine learning based prediction, and meta-analysis

    doi: 10.1371/journal.pone.0257857

    Figure Lengend Snippet: Accuracy, cohen’s κ, TPR and TNR values based for the all ML tools and using either 5-fold cross validation or 20% testing set for prediction people with T2DM from people without T2DM.

    Article Snippet: In both SVM techniques implemented in the WEKA-KNIME LibSVM node, the following default parameters were used: kernel cache (cache size = 40.0), kernel type is radial basis function: exp(-gamma*|u-v|2), loss function is 0.1, kernel coefficients epsilon = 0.001 and Gamma = 0.00.

    Techniques: Biomarker Discovery

    Accuracy, cohen’s κ, TPR, and TNR values based for the all ML tools and using either 5-fold cross validation or 20% testing set for prediction people with T2DM from people without T2DM.

    Journal: PLoS ONE

    Article Title: Investigating the association of CD36 gene polymorphisms (rs1761667 and rs1527483) with T2DM and dyslipidemia: Statistical analysis, machine learning based prediction, and meta-analysis

    doi: 10.1371/journal.pone.0257857

    Figure Lengend Snippet: Accuracy, cohen’s κ, TPR, and TNR values based for the all ML tools and using either 5-fold cross validation or 20% testing set for prediction people with T2DM from people without T2DM.

    Article Snippet: In both SVM techniques implemented in the WEKA-KNIME LibSVM node, the following default parameters were used: kernel cache (cache size = 40.0), kernel type is radial basis function: exp(-gamma*|u-v|2), loss function is 0.1, kernel coefficients epsilon = 0.001 and Gamma = 0.00.

    Techniques: Biomarker Discovery