Journal Article

Glycan classification with tree kernels

Yoshihiro Yamanishi, Francis Bach and Jean-Philippe Vert

in Bioinformatics

Volume 23, issue 10, pages 1211-1216
Published in print May 2007 | ISSN: 1367-4803
Published online March 2007 | e-ISSN: 1460-2059 | DOI: https://dx.doi.org/10.1093/bioinformatics/btm090
Glycan classification with tree kernels

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Motivation: Glycans are covalent assemblies of sugar that play crucial roles in many cellular processes. Recently, comprehensive data about the structure and function of glycans have been accumulated, therefore the need for methods and algorithms to analyze these data is growing fast.

Results: This article presents novel methods for classifying glycans and detecting discriminative glycan motifs with support vector machines (SVM). We propose a new class of tree kernels to measure the similarity between glycans. These kernels are based on the comparison of tree substructures, and take into account several glycan features such as the sugar type, the sugar bound type or layer depth. The proposed methods are tested on their ability to classify human glycans into four blood components: leukemia cells, erythrocytes, plasma and serum. They are shown to outperform a previously published method. We also applied a feature selection approach to extract glycan motifs which are characteristic of each blood component. We confirmed that some leukemia-specific glycan motifs detected by our method corresponded to several results in the literature.

Availability: Softwares are available upon request.

Contact: yoshi@kuicr.kyoto-u.ac.jp

Supplementary information: Datasets are available at the following website: http://web.kuicr.kyoto-u.ac.jp/supp/yoshi/glycankernel/

Journal Article.  4475 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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