Review



weka-knime libsvm node  (KNIME GmbH)

 
  • Logo
  • About
  • News
  • Press Release
  • Team
  • Advisors
  • Partners
  • Contact
  • Bioz Stars
  • Bioz vStars
    • 90
      Buy from Supplier

    Structured Review

    KNIME GmbH weka-knime libsvm node
    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.
    Weka Knime 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/pmc08516279-304-7-7
    Average 90 stars, based on 1 article reviews
    weka-knime libsvm node - by Bioz Stars, 2026-11
    90/100 stars

    Images

    1) Product Images from "Investigating the association of CD36 gene polymorphisms (rs1761667 and rs1527483) with T2DM and dyslipidemia: Statistical analysis, machine learning based prediction, and meta-analysis"

    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

    Journal: PLoS ONE

    doi: 10.1371/journal.pone.0257857

    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.
    Figure Legend 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.

    Techniques Used: 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.
    Figure Legend 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.

    Techniques Used: Biomarker Discovery

    Related Articles

    other:

    Article Title: A study of tour-based mode choice based on a Support Vector Machine classifier
    Article Snippet: A new approach in recognizing travel mode choice patterns is proposed, based on the Support Vector Machine classification technique.. The tour-based travel demand dataset that is analysed is for New York State, derived from the 2009 U.S. National Household Travel Survey.. The main features characterizing each tour are the means used, travel-related variables and socioeconomic aspects.

    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
    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.

    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.

    Plasmid Preparation:

    Article Title: Identifying inhibitors of β-haematin formation with activity against chloroquine-resistant Plasmodium falciparum malaria parasites via virtual screening approaches
    Article Snippet: .. The support vector machine (SVM) model was built using LibSVM, as implemented in the KNIME Analytics Platform , . ..



    Similar Products

    libsvm node  (KNIME GmbH)
    90
    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
    		  Buy from Supplier
    weka-knime libsvm node  (KNIME GmbH)
    90
    KNIME GmbH weka-knime libsvm node
    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.
    Weka Knime 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/pmc08516279-304-7-7
    Average 90 stars, based on 1 article reviews
    weka-knime libsvm node - by Bioz Stars, 2026-11
    90/100 stars
    		  Buy from Supplier

    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