Extracting glycan motifs using a biochemically-weighted kernel



Hao Jiang1, Kiyoko F Aoki-Kinoshita 2* & Wai-Ki Ching1



1Advanced Modeling and Applied Computing Laboratory, Department of Mathematics, University of Hong Kong, Pokfulam Road, Hong Kong;2Department of Bioinformatics, Faculty of Engineering, Soka University, Tokyo, Japan


Email; *Corresponding author


Article Type




Selected publications from Asia Pacific Bioinformatics Network (APBioNet) 10th International Conference on Bioinformatics (InCoB 2011), Malaysia, November 30 to December 02, 2011



Carbohydrates, or glycans, are one of the most abundant and structurally diverse biopolymers constitute the third major class of biomolecules, following DNA and proteins. However, the study of carbohydrate sugar chains has lagged behind compared to that of DNA and proteins, mainly due to their inherent structural complexity. However, their analysis is important because they serve various important roles in biological processes, including signaling transduction and cellular recognition. In order to glean some light into glycan function based on carbohydrate structure, kernel methods have been developed in the past, in particular to extract potential glycan biomarkers by classifying glycan structures found in different tissue samples. The recently developed weighted q-gram method (LK-method) exhibits good performance on glycan structure classification while having limitations in feature selection. That is, it was unable to extract biologically meaningful features from the data. Therefore, we propose a biochemically-weighted tree kernel (BioLK-method) which is based on a glycan similarity matrix and also incorporates biochemical information of individual q-grams in constructing the kernel matrix. We further applied our new method for the classification and recognition of motifs on publicly available glycan data. Our novel tree kernel (BioLK-method) using a Support Vector Machine (SVM) is capable of detecting biologically important motifs accurately while LK-method failed to do so. It was tested on three glycan data sets from the Consortium for Functional Glycomics (CFG) and Kyoto Encyclopedia of Genes and Genomes (KEGG) GLYCAN and showed that the results are consistent with the literature. The newly developed BioLK-method also maintains comparable classification performance with the LK-method. Our results obtained here indicate that the incorporation of biochemical information of q-grams further shows the flexibility and capability of the novel kernel in feature extraction, which may aid in the prediction of glycan biomarkers.



metacestode; rodent; internal transcribed spacer; ribosomal DNA; polymerase chain reaction



Jiang et al. Bioinformation 7(8): 405-412 (2011)

Edited by

TW Tan






Biomedical Informatics



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