Prediction power of the supervised models. (a) ROC curves for five different bins based on the results of the SVM classification models. (b) Predicted versus experimentally measured expression levels. The SVR regression model was applied to bin 1 for predicting gene expression levels. (PCC, Pearson correlation coefficient). (c) The prediction accuracy of SVM classification models for all the 160 bins. For each bin, we constructed an SVM classification model and summarized its accuracy using the AUC score. The AUC scores were calculated based on cross-validation repeated 100 times for each bin. The red curve shows the average AUC scores (mean of 100 repeats) of the bins and the blue bars indicate their standard deviations. The positions of the TSS and TTS are marked by dotted lines.
Cheng et al. Genome Biology 2011 12:R15 doi:10.1186/gb-2011-12-2-r15