Figure 4

ROC curves for prediction of CDK substrate proteins. (a) Comparison of classifiers suggests that cluster based methods S _LR and S _BN (filled squares and triangles, respectively) perform better than the density of strong matches (filled circles). (b-d) comparison of cluster-based method S _LR (filled squares) with Scansite, a matrix-based method (unfilled squares). See text for details. Plotted is the fraction of positives versus the fraction of negatives passing as the threshold is varied in the three datasets a, b ('unbiased' proteins, which were randomly chosen), c ('2+' proteins, which contain two or more matches to the strong CDK consensus), and d ('1cc' proteins containing one match to the strong CDK consensus and whose transcripts exhibit cell-cycle regulation). Note that the unlike conventional ROC curves, we plot the false-positive rate on a log scale, such that the expectation for a random predictor no longer falls on the diagonal. The expectation for a random predictor is indicated in each panel by the dotted trace. CDK, cyclin-dependent kinase; ROC, receiver operating characteristic.