Show simple item record

dc.contributor.convenorHisao Ishibuchi
dc.contributor.authorHiggs, Trent
dc.contributor.authorStantic, Bela
dc.contributor.authorHoque, Md Tamjidul
dc.contributor.authorSattar, Abdul
dc.contributor.editorHisao Ishibuchi
dc.date.accessioned2017-05-03T11:26:31Z
dc.date.available2017-05-03T11:26:31Z
dc.date.issued2010
dc.date.modified2012-09-02T23:16:19Z
dc.identifier.isbn9781424469109
dc.identifier.refurihttp://www.wcci2010.org/topics/ieee-cec-2010
dc.identifier.doi10.1109/CEC.2010.5586149
dc.identifier.urihttp://hdl.handle.net/10072/37314
dc.description.abstractProteins carry out the majority of functionality on a cellular level. Computational protein structure prediction (PSP) methods have been introduced to speed up the PSP process due to manual methods, like nuclear magnetic resonance (NMR) and x-ray crystallography (XC) taking numerous months even years to produce a predicted structure for a target protein. A lot of work in this area is focused on the type of search strategy to employ. Two popular methods in the literature are: Monte Carlo based algorithms and Genetic Algorithms. Genetic Algorithms (GA) have proven to be quite useful in the PSP field, as they allow for a generic search approach, which alleviates the need to redefine the search strategies for separate sequences. They also lend themselves well to feature-based resampling techniques. Feature-based resampling works by taking previously computed local minima and combining features from them to create new structures that are more uniformly low in free energy. In this work we present a feature-based resampling genetic algorithm to refine structures that are outputted by PSP software. Our results indicate that our approach performs well, and produced an average 9.5% root mean square deviation (RMSD) improvement and a 17.36% template modeling score (TM-Score) improvement.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.format.extent834146 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.language.isoeng
dc.publisherIEEE
dc.publisher.placePiscataway, NJ, USA
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofconferencename2010 IEEE World Congress on Computational Intelligence
dc.relation.ispartofconferencetitle2010 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC)
dc.relation.ispartofdatefrom2010-07-18
dc.relation.ispartofdateto2010-07-23
dc.relation.ispartoflocationBarcelona, SPAIN
dc.relation.ispartofpagefrom8 pages
dc.relation.ispartofpageto8 pages
dc.rights.retentionY
dc.subject.fieldofresearchTheory of computation not elsewhere classified
dc.subject.fieldofresearchData engineering and data science
dc.subject.fieldofresearchcode461399
dc.subject.fieldofresearchcode460501
dc.titleGenetic Algorithm Feature-Based Resampling for Protein Structure Prediction
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.rights.copyright© 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.
gro.date.issued2010
gro.hasfulltextFull Text
gro.griffith.authorStantic, Bela
gro.griffith.authorSattar, Abdul


Files in this item

This item appears in the following Collection(s)

  • Conference outputs
    Contains papers delivered by Griffith authors at national and international conferences.

Show simple item record