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Related Experiment Videos

Glycan classification with tree kernels.

Yoshihiro Yamanishi1, Francis Bach, Jean-Philippe Vert

  • 1Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan. yoshi@kuicr.kyoto-u.ac.jp

Bioinformatics (Oxford, England)
|March 9, 2007
PubMed
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Novel methods classify glycans and identify discriminative glycan motifs using tree kernels and support vector machines (SVM). This approach accurately categorizes human glycans in blood components, outperforming existing methods.

Area of Science:

  • Glycomics
  • Bioinformatics
  • Computational Biology

Background:

  • Glycans are crucial sugar assemblies involved in numerous cellular processes.
  • Accumulating glycan data necessitates advanced analytical methods and algorithms.
  • Existing methods for glycan analysis require improvement for complex datasets.

Purpose of the Study:

  • To develop novel methods for classifying glycans and detecting discriminative glycan motifs.
  • To introduce a new class of tree kernels for measuring glycan similarity.
  • To apply these methods for classifying human glycans in different blood components.

Main Methods:

  • Classification of glycans using support vector machines (SVM).
  • Development of novel tree kernels based on comparing glycan substructures.

Related Experiment Videos

  • Incorporation of glycan features like sugar type, bond type, and layer depth into kernel methods.
  • Application of feature selection to identify characteristic glycan motifs.

    • Main Results:

    • The proposed tree kernel methods demonstrated superior performance in classifying human glycans across leukemia cells, erythrocytes, plasma, and serum.
    • The methods outperformed a previously published glycan classification technique.
    • Identified leukemia-specific glycan motifs consistent with existing literature findings.

    Conclusions:

    • The novel tree kernel-based SVM approach provides an effective tool for glycan classification and motif discovery.
    • This method enhances the analysis of complex glycan data, particularly in biomedical applications.
    • The findings contribute to a better understanding of glycan roles in different biological contexts, such as leukemia.