Scheme of two-level 10-fold cross-validation.

<p>The data set is partitioned into 10 parts (folds) in the outer loop. One fold of the data set is kept for testing of SVM. The remaining 9 folds are used as the training set for training an SVM. In the inner loop, the training set is further divided into 10 folds to choose the optimal parameters for testing the accuracy of the data set kept in the outer loop. The procedure is repeated 10 times.</p

The accuracy of SVM using pair of propensities by 2-level 10-fold cross validation.

<p>The accuracy of SVM using pair of propensities by 2-level 10-fold cross validation.</p

The frequency with which each propensity is selected in 1,000 runs of GFSMLP (<i>η</i> = 0.2, <i>μ</i> = 0.1, <i>n</i> = 15, number of iterations = 2,000).

<p>The frequency with which each propensity is selected in 1,000 runs of GFSMLP (<i>η</i> = 0.2, <i>μ</i> = 0.1, <i>n</i> = 15, number of iterations = 2,000).</p

The accuracy of SVM using just single propensity by the 2-level 10-fold cross validation scheme.

<p>The accuracy of SVM using just single propensity by the 2-level 10-fold cross validation scheme.</p

The frequency with which each feature/propensity is selected in 100 runs of GFSMLP (<i>η</i> = 0.2, <i>μ</i> = 0.1, <i>n</i> = 15, number of iterations = 2,000).

<p>The frequency with which each feature/propensity is selected in 100 runs of GFSMLP (<i>η</i> = 0.2, <i>μ</i> = 0.1, <i>n</i> = 15, number of iterations = 2,000).</p

FSMLP network structure.

<p>Eight amino acid propensities are used in the input layer. Each propensity results in 20 normalized amino acid values. Thus the inputs are in 160-dimension. The 20 values corresponding to a particular propensity are treated as a group. The algorithm selects one or more propensities to evaluate its or their performance. After the training is over, the GFSMLP reports the most useful propensity/propensities to classify the input peptide sequence belonging to epitopes or non-epitopes in the output layer.</p

Encoding scheme for the calculation of correlations of pair amino acid propensities.

<p>Encoding scheme for the calculation of correlations of pair amino acid propensities.</p

Average of misclassification rates over 100 runs of GFSMLP using the selected propensity (<i>η</i> = 0.2, <i>μ</i> = 0, <i>n</i> = 15, number of iterations = 2,000).

<p>Average of misclassification rates over 100 runs of GFSMLP using the selected propensity (<i>η</i> = 0.2, <i>μ</i> = 0, <i>n</i> = 15, number of iterations = 2,000).</p

Gd-DTPA concentration as estimated from the ROI in the brain tumor 20 min and 24 h after sonication, ipsilateral brain, and contralateral normal brain.

<p>Gd-DTPA concentration as estimated from the ROI in the brain tumor 20 min and 24 h after sonication, ipsilateral brain, and contralateral normal brain.</p

Estimation of the changes in Gd concentration using GKM or Two-stage GKM.

<p>(A) estimation of the changes in Gd concentration at the right hemisphere of the rat's brain tissue for the single FUS irradiation experiment using GKM; (B) estimation of the changes in Gd concentration in the right hemisphere of the rat's brain tissue for the repeated FUS irradiation experiment using GKM; (C) based on the condition in (B), estimation of the changes in Gd concentration in the right hemisphere of the rat's brain tissue for the repeated FUS irradiation experiment using Two-stage GKM; (D) list of the permeability values K_trans and K_ep estimated from (B) and (C). The data points in the figure show the changes in Gd concentration with time in the right hemisphere of the rat's brain tissue due to FUS irradiation. The curve was estimated using GKM or Two-stage GKM.</p