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Article: Clustering-based approach for predicting motif pairs from protein interaction data

TitleClustering-based approach for predicting motif pairs from protein interaction data
Authors
Issue Date2009
PublisherImperial College Press. The Journal's web site is located at http://www.worldscinet.com/jbcb/jbcb.shtml
Citation
Journal Of Bioinformatics And Computational Biology, 2009, v. 7 n. 4, p. 701-716 How to Cite?
AbstractPredicting motif pairs from a set of protein sequences based on the protein-protein interaction data is an important, but difficult computational problem. Tan et al. proposed a solution to this problem. However, the scoring function (using λ 2 testing) used in their approach is not adequate and their approach is also not scalable. It may take days to process a set of 5000 protein sequences with about 20,000 interactions. Later, Leung et al. proposed an improved scoring function and faster algorithms for solving the same problem. But, the model used in Leung et al. is complicated. The exact value of the scoring function is not easy to compute and an estimated value is used in practice. In this paper, we derive a better model to capture the significance of a given motif pair based on a clustering notion. We develop a fast heuristic algorithm to solve the problem. The algorithm is able to locate the correct motif pair in the yeast data set in about 45 minutes for 5000 protein sequences and 20,000 interactions. Moreover, we derive a lower bound result for the p-value of a motif pair in order for it to be distinguishable from random motif pairs. The lower bound result has been verified using simulated data sets. © 2009 Imperial College Press.
Persistent Identifierhttp://hdl.handle.net/10722/152414
ISSN
2015 Impact Factor: 0.785
2015 SCImago Journal Rankings: 0.525
References

 

DC FieldValueLanguage
dc.contributor.authorLeung, HCMen_US
dc.contributor.authorSiu, MHen_US
dc.contributor.authorYiu, SMen_US
dc.contributor.authorChin, FYLen_US
dc.contributor.authorSung, KWKen_US
dc.date.accessioned2012-06-26T06:38:15Z-
dc.date.available2012-06-26T06:38:15Z-
dc.date.issued2009en_US
dc.identifier.citationJournal Of Bioinformatics And Computational Biology, 2009, v. 7 n. 4, p. 701-716en_US
dc.identifier.issn0219-7200en_US
dc.identifier.urihttp://hdl.handle.net/10722/152414-
dc.description.abstractPredicting motif pairs from a set of protein sequences based on the protein-protein interaction data is an important, but difficult computational problem. Tan et al. proposed a solution to this problem. However, the scoring function (using λ 2 testing) used in their approach is not adequate and their approach is also not scalable. It may take days to process a set of 5000 protein sequences with about 20,000 interactions. Later, Leung et al. proposed an improved scoring function and faster algorithms for solving the same problem. But, the model used in Leung et al. is complicated. The exact value of the scoring function is not easy to compute and an estimated value is used in practice. In this paper, we derive a better model to capture the significance of a given motif pair based on a clustering notion. We develop a fast heuristic algorithm to solve the problem. The algorithm is able to locate the correct motif pair in the yeast data set in about 45 minutes for 5000 protein sequences and 20,000 interactions. Moreover, we derive a lower bound result for the p-value of a motif pair in order for it to be distinguishable from random motif pairs. The lower bound result has been verified using simulated data sets. © 2009 Imperial College Press.en_US
dc.languageengen_US
dc.publisherImperial College Press. The Journal's web site is located at http://www.worldscinet.com/jbcb/jbcb.shtmlen_US
dc.relation.ispartofJournal of Bioinformatics and Computational Biologyen_US
dc.subject.meshAlgorithmsen_US
dc.subject.meshAmino Acid Motifsen_US
dc.subject.meshAmino Acid Sequenceen_US
dc.subject.meshBinding Sitesen_US
dc.subject.meshCluster Analysisen_US
dc.subject.meshMolecular Sequence Dataen_US
dc.subject.meshPattern Recognition, Automated - Methodsen_US
dc.subject.meshProtein Bindingen_US
dc.subject.meshProtein Interaction Mapping - Methodsen_US
dc.subject.meshProtein Structure, Tertiaryen_US
dc.subject.meshProteins - Chemistry - Metabolismen_US
dc.subject.meshSequence Analysis, Protein - Methodsen_US
dc.titleClustering-based approach for predicting motif pairs from protein interaction dataen_US
dc.typeArticleen_US
dc.identifier.emailLeung, HCM:cmleung2@cs.hku.hken_US
dc.identifier.emailYiu, SM:smyiu@cs.hku.hken_US
dc.identifier.emailChin, FYL:chin@cs.hku.hken_US
dc.identifier.authorityLeung, HCM=rp00144en_US
dc.identifier.authorityYiu, SM=rp00207en_US
dc.identifier.authorityChin, FYL=rp00105en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1142/S0219720009004266en_US
dc.identifier.pmid19634199-
dc.identifier.scopuseid_2-s2.0-68349110569en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-68349110569&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume7en_US
dc.identifier.issue4en_US
dc.identifier.spage701en_US
dc.identifier.epage716en_US
dc.publisher.placeUnited Kingdomen_US
dc.identifier.scopusauthoridLeung, HCM=35233742700en_US
dc.identifier.scopusauthoridSiu, MH=36762173800en_US
dc.identifier.scopusauthoridYiu, SM=7003282240en_US
dc.identifier.scopusauthoridChin, FYL=7005101915en_US
dc.identifier.scopusauthoridSung, KWK=12797768900en_US

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