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Article: MotifVoter: A novel ensemble method for fine-grained integration of generic motif finders

TitleMotifVoter: A novel ensemble method for fine-grained integration of generic motif finders
Authors
Issue Date2008
PublisherOxford University Press. The Journal's web site is located at http://bioinformatics.oxfordjournals.org/
Citation
Bioinformatics, 2008, v. 24 n. 20, p. 2288-2295 How to Cite?
Abstract
Motivation: Locating transcription factor binding sites (motifs) is a key step in understanding gene regulation. Based on Tompa's benchmark study, the performance of current de novo motif finders is far from satisfactory (with sensitivity ≤0.222 and precision ≤0.307). The same study also shows that no motif finder performs consistently well over all datasets. Hence, it is not clear which finder one should use for a given dataset. To address this issue, a class of algorithms called ensemble methods have been proposed. Though the existing ensemble methods overall perform better than stand-alone motif finders, the improvement gained is not substantial. Our study reveals that these methods do not fully exploit the information obtained from the results of individual finders, resulting in minor improvement in sensitivity and poor precision. Results: In this article, we identify several key observations on how to utilize the results from individual finders and design a novel ensemble method, MotifVoter, to predict the motifs and binding sites. Evaluations on 186 datasets show that MotifVoter can locate more than 95% of the binding sites found by its component motif finders. In terms of sensitivity and precision, MotifVoter outperforms stand-alone motif finders and ensemble methods significantly on Tompa's benchmark, Escherichia coli, and ChIP-Chip datasets. MotifVoter is available online via a web server with several biologist-friendly features. © The Author 2008. Published by Oxford University Press. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/60600
ISSN
2013 Impact Factor: 4.621
ISI Accession Number ID
Funding AgencyGrant Number
National University of SingaporeR-252-000326-112
Research Output Prize (Faculty of Engineering) of the University of HongKong
Funding Information:

National University of Singapore (grant R-252-000326-112); Research Output Prize (Faculty of Engineering) of the University of HongKong to S.M.Y.

References

 

DC FieldValueLanguage
dc.contributor.authorWijaya, Een_HK
dc.contributor.authorYiu, SMen_HK
dc.contributor.authorSon, NTen_HK
dc.contributor.authorKanagasabai, Ren_HK
dc.contributor.authorSung, WKen_HK
dc.date.accessioned2010-05-31T04:14:45Z-
dc.date.available2010-05-31T04:14:45Z-
dc.date.issued2008en_HK
dc.identifier.citationBioinformatics, 2008, v. 24 n. 20, p. 2288-2295en_HK
dc.identifier.issn1367-4803en_HK
dc.identifier.urihttp://hdl.handle.net/10722/60600-
dc.description.abstractMotivation: Locating transcription factor binding sites (motifs) is a key step in understanding gene regulation. Based on Tompa's benchmark study, the performance of current de novo motif finders is far from satisfactory (with sensitivity ≤0.222 and precision ≤0.307). The same study also shows that no motif finder performs consistently well over all datasets. Hence, it is not clear which finder one should use for a given dataset. To address this issue, a class of algorithms called ensemble methods have been proposed. Though the existing ensemble methods overall perform better than stand-alone motif finders, the improvement gained is not substantial. Our study reveals that these methods do not fully exploit the information obtained from the results of individual finders, resulting in minor improvement in sensitivity and poor precision. Results: In this article, we identify several key observations on how to utilize the results from individual finders and design a novel ensemble method, MotifVoter, to predict the motifs and binding sites. Evaluations on 186 datasets show that MotifVoter can locate more than 95% of the binding sites found by its component motif finders. In terms of sensitivity and precision, MotifVoter outperforms stand-alone motif finders and ensemble methods significantly on Tompa's benchmark, Escherichia coli, and ChIP-Chip datasets. MotifVoter is available online via a web server with several biologist-friendly features. © The Author 2008. Published by Oxford University Press. All rights reserved.en_HK
dc.languageengen_HK
dc.publisherOxford University Press. The Journal's web site is located at http://bioinformatics.oxfordjournals.org/en_HK
dc.relation.ispartofBioinformaticsen_HK
dc.rightsBioinformatics. Copyright © Oxford University Press.en_HK
dc.subject.meshComputational Biology - methods-
dc.subject.meshRegulatory Elements, Transcriptional-
dc.subject.meshTranscription Factors - chemistry - metabolism-
dc.subject.meshProtein Structure, Tertiary-
dc.titleMotifVoter: A novel ensemble method for fine-grained integration of generic motif findersen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1367-4803&volume=24&issue=20&spage=2288&epage=2295&date=2008&atitle=MotifVoter:+a+novel+ensemble+method+for+fine-grained+integration+of+generic+motif+findersen_HK
dc.identifier.emailYiu, SM:smyiu@cs.hku.hken_HK
dc.identifier.authorityYiu, SM=rp00207en_HK
dc.description.naturelink_to_OA_fulltext-
dc.identifier.doi10.1093/bioinformatics/btn420en_HK
dc.identifier.pmid18697768en_HK
dc.identifier.scopuseid_2-s2.0-53749085875en_HK
dc.identifier.hkuros161318en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-53749085875&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume24en_HK
dc.identifier.issue20en_HK
dc.identifier.spage2288en_HK
dc.identifier.epage2295en_HK
dc.identifier.eissn1460-2059-
dc.identifier.isiWOS:000259973500003-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridWijaya, E=26326005500en_HK
dc.identifier.scopusauthoridYiu, SM=7003282240en_HK
dc.identifier.scopusauthoridSon, NT=25227847800en_HK
dc.identifier.scopusauthoridKanagasabai, R=23135246200en_HK
dc.identifier.scopusauthoridSung, WK=13310059700en_HK
dc.identifier.citeulike3132697-

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