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Article: A weighted q-gram method for glycan structure classification

TitleA weighted q-gram method for glycan structure classification
Authors
KeywordsBiology
Computer applications
Issue Date2010
PublisherBioMed Central Ltd. The Journal's web site is located at http://www.biomedcentral.com/bmcbioinformatics/
Citation
Bmc Bioinformatics, 2010, v. 11 SUPPLL.1 How to Cite?
AbstractBackground: Glycobiology pertains to the study of carbohydrate sugar chains, or glycans, in a particular cell or organism. Many computational approaches have been proposed for analyzing these complex glycan structures, which are chains of monosaccharides. The monosaccharides are linked to one another by glycosidic bonds, which can take on a variety of comformations, thus forming branches and resulting in complex tree structures. The q-gram method is one of these recent methods used to understand glycan function based on the classification of their tree structures. This q-gram method assumes that for a certain q, different q-grams share no similarity among themselves. That is, that if two structures have completely different components, then they are completely different. However, from a biological standpoint, this is not the case. In this paper, we propose a weighted q-gram method to measure the similarity among glycans by incorporating the similarity of the geometric structures, monosaccharides and glycosidic bonds among q-grams. In contrast to the traditional q-gram method, our weighted q-gram method admits similarity among q-grams for a certain q. Thus our new kernels for glycan structure were developed and then applied in SVMs to classify glycans.Results: Two glycan datasets were used to compare the weighted q-gram method and the original q-gram method. The results show that the incorporation of q-gram similarity improves the classification performance for all of the important glycan classes tested.Conclusion: The results in this paper indicate that similarity among q-grams obtained from geometric structure, monosaccharides and glycosidic linkage contributes to the glycan function classification. This is a big step towards the understanding of glycan function based on their complex structures. © 2010 Li et al; licensee BioMed Central Ltd.
Persistent Identifierhttp://hdl.handle.net/10722/75218
ISSN
2015 Impact Factor: 2.435
2015 SCImago Journal Rankings: 1.722
PubMed Central ID
ISI Accession Number ID
Funding AgencyGrant Number
HKRGC7017/07P
HKUCRGC
HKU Strategy Research Theme fund on Computational Sciences
Hung Hing Ying Physical Research Sciences
National Natural Science Foundation of China10971075
Guangdong Provincial Natural Science9151063101000021
Funding Information:

The authors would like to thank the two anonymous referees and the editor for their helpful suggestions and corrections. Research supported in part by HKRGC Grant No. 7017/07P, HKUCRGC Grants, HKU Strategy Research Theme fund on Computational Sciences, Hung Hing Ying Physical Research Sciences Research Grant, National Natural Science Foundation of China Grant No. 10971075 and Guangdong Provincial Natural Science Grant No. 9151063101000021.

References

 

DC FieldValueLanguage
dc.contributor.authorLi, Len_HK
dc.contributor.authorChing, WKen_HK
dc.contributor.authorYamaguchi, Ten_HK
dc.contributor.authorAokiKinoshita, KFen_HK
dc.date.accessioned2010-09-06T07:09:03Z-
dc.date.available2010-09-06T07:09:03Z-
dc.date.issued2010en_HK
dc.identifier.citationBmc Bioinformatics, 2010, v. 11 SUPPLL.1en_HK
dc.identifier.issn1471-2105en_HK
dc.identifier.urihttp://hdl.handle.net/10722/75218-
dc.description.abstractBackground: Glycobiology pertains to the study of carbohydrate sugar chains, or glycans, in a particular cell or organism. Many computational approaches have been proposed for analyzing these complex glycan structures, which are chains of monosaccharides. The monosaccharides are linked to one another by glycosidic bonds, which can take on a variety of comformations, thus forming branches and resulting in complex tree structures. The q-gram method is one of these recent methods used to understand glycan function based on the classification of their tree structures. This q-gram method assumes that for a certain q, different q-grams share no similarity among themselves. That is, that if two structures have completely different components, then they are completely different. However, from a biological standpoint, this is not the case. In this paper, we propose a weighted q-gram method to measure the similarity among glycans by incorporating the similarity of the geometric structures, monosaccharides and glycosidic bonds among q-grams. In contrast to the traditional q-gram method, our weighted q-gram method admits similarity among q-grams for a certain q. Thus our new kernels for glycan structure were developed and then applied in SVMs to classify glycans.Results: Two glycan datasets were used to compare the weighted q-gram method and the original q-gram method. The results show that the incorporation of q-gram similarity improves the classification performance for all of the important glycan classes tested.Conclusion: The results in this paper indicate that similarity among q-grams obtained from geometric structure, monosaccharides and glycosidic linkage contributes to the glycan function classification. This is a big step towards the understanding of glycan function based on their complex structures. © 2010 Li et al; licensee BioMed Central Ltd.en_HK
dc.languageengen_HK
dc.publisherBioMed Central Ltd. The Journal's web site is located at http://www.biomedcentral.com/bmcbioinformatics/en_HK
dc.relation.ispartofBMC Bioinformaticsen_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectBiology-
dc.subjectComputer applications-
dc.titleA weighted q-gram method for glycan structure classificationen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1471-2105&volume=11&issue=Suppl 1 article no. S33&spage=&epage=&date=2010&atitle=A+Weighted+q-gram+method+for+glycan+classificationen_HK
dc.identifier.emailChing, WK:wching@hku.hken_HK
dc.identifier.authorityChing, WK=rp00679en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1186/1471-2105-11-S1-S33en_HK
dc.identifier.pmid20122206-
dc.identifier.pmcidPMC3009505-
dc.identifier.scopuseid_2-s2.0-75149152441en_HK
dc.identifier.hkuros168808en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-75149152441&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume11en_HK
dc.identifier.issueSUPPLL.1en_HK
dc.identifier.isiWOS:000277537900014-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridLi, L=35329863000en_HK
dc.identifier.scopusauthoridChing, WK=13310265500en_HK
dc.identifier.scopusauthoridYamaguchi, T=35331147100en_HK
dc.identifier.scopusauthoridAokiKinoshita, KF=8704411700en_HK
dc.identifier.citeulike6602100-

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