Performance of ordered and disordered residue classification based on per residue PSEE value calculated using different contact radius (CR) values.

<p>Classification performance is shown in terms of (A) ACC (<i>blue bar</i>), (B) PPV (<i>purple bar</i>) and (C) MCC (<i>green bar</i>) for CR values equal to 4 to 30. The x-axis and y-axis show the CR values and the performance metric values, respectively.</p

ROC curves given by DisPredict for the probability prediction per residue while the training is performed with (A) SL477 and (B) MxD444 dataset.

<p>In each figure, the solid (<i>blue</i>) curve corresponds to the cross validation test on the same dataset and the dotted (<i>red</i>) curve corresponds to the independent test. The AUC values given in each figure correspond to the values in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0141551#pone.0141551.t006" target="_blank">Table 6</a>. The x-axis and y-axis show the Specificity and Sensitivity, respectively.</p

Overview of feature aggregation, optimization-layer and classification-layer in DisPredict.

<p>In the feature aggregation step, features are shown in their abbreviated form according to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0141551#pone.0141551.t001" target="_blank">Table 1</a> and the arrows are labeled by the number of features involved. The classification-layer receives final feature set from the feature aggregation step and optimal parameters from the optimization-layer. Then, it generates the predictor model and outputs both binary annotation and real-valued class probabilities.</p

Optimized Parameters used to build DisPredict Models.

<p><i>C</i> is the soft penalty parameter to handle overlapped class.</p><p><i>γ</i> is the parameter for radial basis kernel for SVM.</p><p>Optimized Parameters used to build DisPredict Models.</p

Performane comparison among DisPredict, SPINE-D and MFDp on independent DD73 dataset.

<p>Best results are marked in bold.</p><p>* Window size = 21, <i>C</i> = 2.0 and <i>γ</i> = 0.0078125.</p><p>Performane comparison among DisPredict, SPINE-D and MFDp on independent DD73 dataset.</p

Distribution of disordered regions of different lengths in MxD444 (<i>left</i>) and SL477 (<i>right</i>) dataset.

<p>Legends are shown for different range of lengths (with interval size 15) and each bar is labeled with total number of occurrence of a disordered region of this specific length.</p

ROC and precision-recall curves given by 8 disorder predictors for DD73 dataset.

<p>Comparison of disorder predictors in terms of (A) ROC curve and (B) precision-recall curve on DD73 dataset. The area under ROC curves are given in the plot (A).</p

Correlation plot between structural characterizations of ordered (<i>blue</i>) and disordered (<i>red</i>) regions within (A) SL477 and (B) MxD444 dataset.

<p>The x-axis and y-axis correspond to the probability of having well defined secondary structure (in terms of probability being coil) and fraction of exposed residues of that region, respectively.</p

ROC and precision-recall curves given by 8 disorder predictors for CASP9 dataset.

<p>Comparison of disorder predictors in terms of (A) ROC curve and (B) precision-recall curve on CASP9 dataset. The area under ROC curves are given in the plot (A).</p

Correlation between mean PSEE and hydrophobicity index of 20 amino acids.

<p>Mean PSEE (<i>blue bar</i>) and hydrophobicity index (<i>red bar</i>) of 20 different types of amino acid residues of SSD1299 dataset. The data values are given in the data table under the plot.</p