alexa A gram distribution kernel applied to glycan classification and motif extraction.


Journal of Glycobiology

Author(s): Kuboyama T, Hirata K, AokiKinoshita KF, Kashima H, Yasuda H

Abstract Share this page

Abstract We propose a novel general-purpose tree kernel and apply it to glycan structure analysis. Our kernel measures the similarity between two labeled trees by counting the number of common q-length substrings (tree q-grams) embedded in the trees for all possible lengths q. We apply our tree kernel using a support vector machine (SVM) to classification and specific feature extraction from glycan structure data. Our results show that our kernel outperforms the layered trimer kernel of Hizukuri et al. which is well tailored to glycan data while we do not adjust our kernel to glycan-specific properties. In addition, we extract specific features from various types of glycan data using our trained SVM. The results show that our kernel is more flexible and capable of finding a wider variety of substructures from glycan data.
This article was published in Genome Inform and referenced in Journal of Glycobiology

Relevant Expert PPTs

Relevant Speaker PPTs

Recommended Conferences

  • 2nd International Conference on Biochemistry
    Sep 21-22, 2017 Macau, Hong Kong
  • International Conference on Glycobiology
    Oct 02-04, 2017 Atlanta, USA
Peer Reviewed Journals
Make the best use of Scientific Research and information from our 700 + peer reviewed, Open Access Journals
International Conferences 2017-18
Meet Inspiring Speakers and Experts at our 3000+ Global Annual Meetings

Contact Us

© 2008-2017 OMICS International - Open Access Publisher. Best viewed in Mozilla Firefox | Google Chrome | Above IE 7.0 version