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

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

Journal: Journal of Cheminformatics

Article Title: Modeling of non-additive mixture properties using the Online CHEmical database and Modeling environment (OCHEM)

doi: 10.1186/1758-2946-5-4

Figure Lengend Snippet: Statistical parameters of Online Chemical Modeling Environment (OCHEM, http://ochem.eu ) models with the lowest RMSE according to the “ compounds-out ” cross-validation protocol for the prediction of binary mixture densities

Article Snippet: Using these descriptors, the results for the test set prediction calculated by LibSVM with ChemAxon descriptors were R 2 = 0.94 ± 0.02, RMSE = 0.006 ± 0.001 – the highest prediction accuracy achieved for this test.

Techniques: Biomarker Discovery