File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Conference Paper: An evolutionary Monte Carlo algorithm for identifying short adjacent repeats in multiple sequences

TitleAn evolutionary Monte Carlo algorithm for identifying short adjacent repeats in multiple sequences
Authors
KeywordsEvolutionary Monte Carlo
Parallel tempering
Repetitive pattern
Sequence motif
Short adjacent repeats
Issue Date2010
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001586
Citation
The 2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Hong Kong, China, 18-21 December 2010. In Proceedings of BIBM, 2010, p. 643-648 How to Cite?
AbstractEvolutionary Monte Carlo (EMC) algorithm is an effective and powerful method to sample complicated distributions. Short adjacent repeats identification problem (SARIP), i.e., searching for the common sequence pattern in multiple DNA sequences, is considered as one of the key challenges in the field of bioinformatics. A recently proposed Markov chain Monte Carlo (MCMC) algorithm has demonstrated its effectiveness in solving SARIP. However, high computation time and inevitable local optima hinder its wide application. In this paper, we apply EMC to parallelize the MCMC algorithm to solve SARIP. Our proposed EMC scheme is implemented on a parallel platform and the simulation results show that, compared with the conventional MCMC algorithm, EMC not only improves the quality of final solution but also reduces the computation time. ©2010 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/142819
ISBN
References

 

DC FieldValueLanguage
dc.contributor.authorXu, Jen_HK
dc.contributor.authorLi, Qen_HK
dc.contributor.authorFan, Xen_HK
dc.contributor.authorLi, VOKen_HK
dc.contributor.authorLi, SYRen_HK
dc.date.accessioned2011-10-28T02:56:07Z-
dc.date.available2011-10-28T02:56:07Z-
dc.date.issued2010en_HK
dc.identifier.citationThe 2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Hong Kong, China, 18-21 December 2010. In Proceedings of BIBM, 2010, p. 643-648en_HK
dc.identifier.isbn978-1-4244-8305-1-
dc.identifier.urihttp://hdl.handle.net/10722/142819-
dc.description.abstractEvolutionary Monte Carlo (EMC) algorithm is an effective and powerful method to sample complicated distributions. Short adjacent repeats identification problem (SARIP), i.e., searching for the common sequence pattern in multiple DNA sequences, is considered as one of the key challenges in the field of bioinformatics. A recently proposed Markov chain Monte Carlo (MCMC) algorithm has demonstrated its effectiveness in solving SARIP. However, high computation time and inevitable local optima hinder its wide application. In this paper, we apply EMC to parallelize the MCMC algorithm to solve SARIP. Our proposed EMC scheme is implemented on a parallel platform and the simulation results show that, compared with the conventional MCMC algorithm, EMC not only improves the quality of final solution but also reduces the computation time. ©2010 IEEE.en_HK
dc.languageengen_US
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001586-
dc.relation.ispartofProceedings of the IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010en_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rightsProceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Copyright © I E E E.-
dc.rights©2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.subjectEvolutionary Monte Carloen_HK
dc.subjectParallel temperingen_HK
dc.subjectRepetitive patternen_HK
dc.subjectSequence motifen_HK
dc.subjectShort adjacent repeatsen_HK
dc.titleAn evolutionary Monte Carlo algorithm for identifying short adjacent repeats in multiple sequencesen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=978-1-4244-8305-1&volume=&spage=643&epage=648&date=2010&atitle=An+evolutionary+Monte+Carlo+algorithm+for+identifying+short+adjacent+repeats+in+multiple+sequences-
dc.identifier.emailLi, VOK:vli@eee.hku.hken_HK
dc.identifier.authorityLi, VOK=rp00150en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/BIBM.2010.5706645en_HK
dc.identifier.scopuseid_2-s2.0-79952390937en_HK
dc.identifier.hkuros196910en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-79952390937&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage643en_HK
dc.identifier.epage648en_HK
dc.description.otherThe 2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Hong Kong, China, 18-21 December 2010. In Proceedings of BIBM, 2010, p. 643-648-
dc.identifier.scopusauthoridXu, J=36242579700en_HK
dc.identifier.scopusauthoridLi, Q=37013647100en_HK
dc.identifier.scopusauthoridFan, X=35302470300en_HK
dc.identifier.scopusauthoridLi, VOK=7202621685en_HK
dc.identifier.scopusauthoridLi, SYR=9335730900en_HK

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats