Principal component scores with loading biplot.

<p>Two principal components are explained almost all of the variability in the performance measures set. The first principal component accounted for 71.50% while the second principal component accounted for 28.40% of the variance of the performance measures data. Seven variables are loaded on the first principal component (AR: Accuracy rate, SP: Specificity, PPV: Positive predictive value, bAR: Balanced accuracy rate, FS: F score, MCC: Matthews correlation coefficient, κ: Kappa) whereas three variables (SE: Sensitivity, NPV: Negative predictive value, DR: Detection rate) are loaded on the second principal component.</p

Plot tab of the MLViS web-tool.

<p>A dendrogram and a heat map can be created based on the compounds’ molecular similarity.</p

Data upload tab of the MLViS web-tool.

<p>Users can upload their files using upload file, paste data or single molecule options.</p

PubChem tab of the MLViS web-tool.

<p>Users can create and view molecular structures of compounds.</p

Performance assessment of various statistical learning algorithms in virtual screening of compounds.

<p>AR: Accuracy rate, SE: Sensitivity, SP: Specificity, PPV: Positive predictive value, NPV: Negative predictive value, DR: Detection rate, bAR: Balanced accuracy rate,</p><p>FS: F score, MCC: Matthews correlation coefficient, κ: Kappa statistic. Bold values indicate the top three winner algorithms in each performance measure</p><p>Performance assessment of various statistical learning algorithms in virtual screening of compounds.</p

Hierarchical cluster dendrogram.

<p>The algorithms used in the study are clustered into five clusters. Cluster 1 and 2 involve the algorithms (RLDA: Robust linear discriminant analysis, bagKNN: Bagged k-nearest neighbors, MDA: Mixture discriminant analysis, KNN: k-Nearest neighbors, SVMrbf: Support vector machines with radial basis function kernel, FDA: Flexible discriminant analysis, J48, C5.0, NN: Neural networks, SVMlin: Support vector machines with linear kernel, lsSVMrbf: Least squares support vector machines with radial basis function kernel, RF: Random forests, bagSVM: Bagged support vector machines), which are loaded on the positive side of the first principal component, and cluster 3 to 5 include the algorithms (LDA: Linear discriminant analysis, lsSVMlin: Least squares support vector machines with linear kernel, NSC: Nearest shrunken centroids, PLS: Partial least squares, QDA: Quadratic discriminant analysis, RQDA: Robust quadratic discriminant analysis, CIT: Conditional inference tree, NB: Naïve bayes, LVQ: Learning vector quantization, CART: Classification and regression trees) that are loaded on the negative side of the first principal component.</p

Genewise dispersion estimations for real datasets.

<p>Genewise dispersion estimations for real datasets.</p

Simulation results for <i>k</i> = 2, <i>d</i>_<i>kj</i> = 10%, transformation: rlog.

<p>Figure shows the performance results of classifiers with changing parameters of sample size (<i>n</i>), number of genes (<i>p</i>) and type of dispersion (<i>φ = 0</i>.<i>01</i>: very slight, <i>φ = 0</i>.<i>1</i>: substantial, <i>φ = 1</i>: very high).</p

Description of real RNA-Seq datasets used in this study.

<p>Description of real RNA-Seq datasets used in this study.</p

Results obtained from real datasets.

<p>Figure shows the performance results of classifiers for datasets with changing number of most significant number of genes. Note that PLDA and NBLDA methods are not performed on the transformed data. However, the results for both transformed and non-transformed data are given in the same figure for the comparison purpose.</p