The workflow of the proposed two-round support vector regression method with weighted degree kernels. (a) The input training DNA binding site sequences with their corresponding K
values, demonstrating the general form of the inputs. (b) The weighted degree kernel matrix of the first round, calculated from Eq. 2. Each dimension lists the training binding sequences as shown in (a), and the corresponding entry value represents the similarity between the two sequences by the WD kernel. (c) Based on the kernel matrix in (b), we did the first round of support vector regression to select the top ten k-mers that contribute most to the high binding affinity (in blue) and the ten k-mers that contribute the most to the low binding affinity (in red). The local optimistic parameters were also selected from this step. (d) The regression of Round 2 to predict binding affinities by using the selected k-mers in a new WD kernel.