Glycan classification with tree kernels

Yoshihiro Yamanishi, Francis Bach, Jean Philippe Vert

    Research output: Contribution to journalArticle

    37 Citations (Scopus)

    Abstract

    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.

    Original languageEnglish
    Pages (from-to)1211-1216
    Number of pages6
    JournalBioinformatics
    Volume23
    Issue number10
    DOIs
    Publication statusPublished - May 15 2007

    All Science Journal Classification (ASJC) codes

    • Statistics and Probability
    • Biochemistry
    • Molecular Biology
    • Computer Science Applications
    • Computational Theory and Mathematics
    • Computational Mathematics

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    Yamanishi, Y., Bach, F., & Vert, J. P. (2007). Glycan classification with tree kernels. Bioinformatics, 23(10), 1211-1216. https://doi.org/10.1093/bioinformatics/btm090