Skip Navigation


Bioinformatics Advance Access originally published online on March 7, 2007
Bioinformatics 2007 23(10):1211-1216; doi:10.1093/bioinformatics/btm090
This Article
Right arrow Full Text Freely available
Right arrow FREE Full Text (Print PDF) Freely available
Right arrow All Versions of this Article:
23/10/1211    most recent
btm090v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrow Search for citing articles in:
ISI Web of Science (1)
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Yamanishi, Y.
Right arrow Articles by Vert, J.-P.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Yamanishi, Y.
Right arrow Articles by Vert, J.-P.
Social Bookmarking
 Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author 2007. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Glycan classification with tree kernels

Yoshihiro Yamanishi 1,*, Francis Bach 2 and Jean-Philippe Vert 3

1Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan, 2Center of Mathematical Morphology, 3Center for Computational Biology, Ecole des Mines de Paris, Fontainebleau, France

*To whom correspondence should be addressed.


   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.

Availability: Softwares are available upon request.

Contact: yoshi{at}kuicr.kyoto-u.ac.jp

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

Associate Editor: Anna Tramontano


Received on December 6, 2006; revised on February 1, 2007; accepted on March 5, 2007

Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
BioinformaticsHome page
K. Hashimoto, I. Takigawa, M. Shiga, M. Kanehisa, and H. Mamitsuka
Mining significant tree patterns in carbohydrate sugar chains
Bioinformatics, August 15, 2008; 24(16): i167 - i173.
[Abstract] [Full Text] [PDF]



Disclaimer:
Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.