A gram distribution kernel applied to glycan classification and motif extraction.

We propose a novel general-purpose tree kernel and apply it to glycan structure analysis. Our kernel measures the similarity between two labeled trees by counting the number of common q-length substrings (tree q-grams) embedded in the trees for all possible lengths q. We apply our tree kernel using a support vector machine (SVM) to classification and specific feature extraction from glycan structure data.