Examining the annealing schedules for RNA design algorithm

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dc.citation.epage1302en_US
dc.citation.spage1295en_US
dc.contributor.authorErhan, H. E.en_US
dc.contributor.authorSav, Sinemen_US
dc.contributor.authorKalashnikov, S.en_US
dc.contributor.authorTsang, H. H.en_US
dc.coverage.spatialVancouver, BC, Canada
dc.date.accessioned2018-04-12T11:41:17Z
dc.date.available2018-04-12T11:41:17Z
dc.date.issued2016-07en_US
dc.departmentDepartment of Computer Engineeringen_US
dc.descriptionDate of Conference: 24-29 July 2016
dc.descriptionConference name: IEEE Congress on Evolutionary Computation (CEC), 2016
dc.description.abstractRNA structures are important for many biological processes in the cell. One important function of RNA are as catalytic elements. Ribozymes are RNA sequences that fold to form active structures that catalyze important chemical reactions. The folded structure for these RNA are very important; only specific conformations maintain these active structures, so it is very important for RNA to fold in a specific way. The RNA design problem describes the prediction of an RNA sequence that will fold into a given RNA structure. Solving this problem allows researchers to design RNA; they can decide on what folded secondary structure is required to accomplish a task, and the algorithm will give them a primary sequence to assemble. However, there are far too many possible primary sequence combinations to test sequentially to see if they would fold into the structure. Therefore we must employ heuristics algorithms to attempt to solve this problem. This paper introduces SIMARD, an evolutionary algorithm that uses an optimization technique called simulated annealing to solve the RNA design problem. We analyzes three different cooling schedules for the annealing process: 1) An adaptive cooling schedule, 2) a geometric cooling schedule, and 3) a geometric cooling schedule with warm up. Our results show that an adaptive annealing schedule may not be more effective at minimizing the Hamming distance between the target structure and our folded sequence's structure when compared with geometric schedules. The results also show that warming up in a geometric cooling schedule may be useful for optimizing SIMARD. © 2016 IEEE.en_US
dc.description.provenanceMade available in DSpace on 2018-04-12T11:41:17Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 179475 bytes, checksum: ea0bedeb05ac9ccfb983c327e155f0c2 (MD5) Previous issue date: 2016en
dc.identifier.doi10.1109/CEC.2016.7743937en_US
dc.identifier.urihttp://hdl.handle.net/11693/37479
dc.language.isoEnglishen_US
dc.publisherIEEEen_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/CEC.2016.7743937en_US
dc.source.titleIEEE Congress on Evolutionary Computation, CEC 2016en_US
dc.subjectAnnealingen_US
dc.subjectCoolingen_US
dc.subjectEvolutionary algorithmsen_US
dc.subjectGeometryen_US
dc.subjectHamming distanceen_US
dc.subjectOptimizationen_US
dc.subjectProblem solvingen_US
dc.subjectRNAen_US
dc.subjectSimulated annealingen_US
dc.subjectAdaptive cooling scheduleen_US
dc.subjectBiological processen_US
dc.subjectCatalytic elementen_US
dc.subjectFolded structuresen_US
dc.subjectHeuristics algorithmen_US
dc.subjectOptimization techniquesen_US
dc.subjectPrimary sequencesen_US
dc.subjectSecondary structuresen_US
dc.subjectBioinformaticsen_US
dc.titleExamining the annealing schedules for RNA design algorithmen_US
dc.typeConference Paperen_US

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