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dc.contributor.convenorHisao Ishibuchien_US
dc.contributor.authorHiggs, Trenten_US
dc.contributor.authorStantic, Belaen_US
dc.contributor.authorHoque, Mden_US
dc.contributor.authorSattar, Abdulen_US
dc.contributor.editorHisao Ishibuchien_US
dc.date.accessioned2017-05-03T11:26:31Z
dc.date.available2017-05-03T11:26:31Z
dc.date.issued2010en_US
dc.date.modified2012-09-02T23:16:19Z
dc.identifier.refurihttp://www.wcci2010.org/topics/ieee-cec-2010en_US
dc.identifier.doi10.1109/CEC.2010.5586149en_US
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.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_US
dc.format.extent834146 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglishen_US
dc.language.isoen_US
dc.publisherIEEEen_US
dc.publisher.placePiscataway, NJ, USAen_US
dc.relation.ispartofstudentpublicationNen_US
dc.relation.ispartofconferencename2010 IEEE World Congress on Computational Intelligence (WCCI)en_US
dc.relation.ispartofconferencetitle2010 IEEE World Congress on Computational Intelligence (WCCI 2010) Proceedingsen_US
dc.relation.ispartofdatefrom2010-07-18en_US
dc.relation.ispartofdateto2010-07-23en_US
dc.relation.ispartoflocationBarcelona, Spainen_US
dc.rights.retentionYen_US
dc.subject.fieldofresearchData Format not elsewhere classifieden_US
dc.subject.fieldofresearchComputation Theory and Mathematics not elsewhere classifieden_US
dc.subject.fieldofresearchcode080499en_US
dc.subject.fieldofresearchcode080299en_US
dc.titleGenetic Algorithm Feature-Based Resampling for Protein Structure Predictionen_US
dc.typeConference outputen_US
dc.type.descriptionE1 - Conference Publications (HERDC)en_US
dc.type.codeE - Conference Publicationsen_US
gro.facultyGriffith Sciences, School of Information and Communication Technologyen_US
gro.rights.copyrightCopyright 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.en_US
gro.date.issued2010
gro.hasfulltextFull Text


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