Figure 3.

Selecting the optimal sequence/structure representation by comparing ROC curves for different kernel designs. Shown are the averaged ROC curves computed using 5-fold cross-validation over complexes in DBD 3.0. The inset shows the true positive rate (TPR) vs. false positive rate (FPR) for up to first 10 % false positives. The legend shows the AUC scores for the different kernels used. (a) Results for different residue kernels K_r using the pairwise kernel K_pw = K_mlpk + K_tppk + K_sum. The curves illustrate the increase in performance as additional structural information is added to the sequence-based kernel. Recall that K_profile is the PSI-BLAST profile kernel; K_prASA uses predicted rASA; K_exp is the residue exposure kernel; K_HSAAC is the half-sphere exposure kernel; K_CX uses protrusion-index features. (b) Results for different pairwise kernels K_pw with residue kernel K_r = K_profile + K_exp + K_HSAAC + K_CX.