SIMARD: a simulated annealing based RNA design algorithm with quality pre-selection strategies
dc.citation.epage | 8 | en_US |
dc.citation.spage | 1 | en_US |
dc.contributor.author | Sav, Sinem | en_US |
dc.contributor.author | Hampson, D. J. D. | en_US |
dc.contributor.author | Tsang, H. H. | en_US |
dc.coverage.spatial | Athens, Greece | |
dc.date.accessioned | 2018-04-12T11:44:26Z | |
dc.date.available | 2018-04-12T11:44:26Z | |
dc.date.issued | 2017-12 | en_US |
dc.department | Department of Computer Engineering | en_US |
dc.description | Date of Conference: 6-9 Dec. 2016 | |
dc.description | Conference name: IEEE Symposium Series on Computational Intelligence (SSCI) 2016 | |
dc.description.abstract | Most of the biological processes including expression levels of genes and translation of DNA to produce proteins within cells depend on RNA sequences, and the structure of the RNA plays vital role for its function. RNA design problem refers to the design of an RNA sequence that folds into given secondary structure. However, vast number of possible nucleotide combinations make this an NP-Hard problem. To solve the RNA design problem, a number of researchers have tried to implement algorithms using local stochastic search, context-free grammars, global sampling or evolutionary programming approaches. In this paper, we examine SIMARD, an RNA design algorithm that implements simulated annealing techniques. We also propose QPS, a mutation operator for SIMARD that pre-selects high quality sequences. Furthermore, we present experiment results of SIMARD compared to eight other RNA design algorithms using the Rfam datset. The experiment results indicate that SIMARD shows promising results in terms of Hamming distance between designed sequence and the target structure, and outperforms ERD in terms of free energy. © 2016 IEEE. | en_US |
dc.description.provenance | Made available in DSpace on 2018-04-12T11:44:26Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 179475 bytes, checksum: ea0bedeb05ac9ccfb983c327e155f0c2 (MD5) Previous issue date: 2017 | en |
dc.identifier.doi | 10.1109/SSCI.2016.7849957 | en_US |
dc.identifier.uri | http://hdl.handle.net/11693/37577 | en_US |
dc.language.iso | English | en_US |
dc.publisher | IEEE | en_US |
dc.relation.isversionof | http://dx.doi.org/10.1109/SSCI.2016.7849957 | en_US |
dc.source.title | IEEE Symposium Series on Computational Intelligence, SSCI 2016 | en_US |
dc.subject | Artificial intelligence | en_US |
dc.subject | Computational complexity | en_US |
dc.subject | Computer programming | en_US |
dc.subject | Context free grammars | en_US |
dc.subject | DNA sequences | en_US |
dc.subject | Free energy | en_US |
dc.subject | Gene expression | en_US |
dc.subject | Hamming distance | en_US |
dc.subject | Nucleic acids | en_US |
dc.subject | Problem solving | en_US |
dc.subject | RNA | en_US |
dc.subject | Simulated annealing | en_US |
dc.subject | Stochastic systems | en_US |
dc.subject | Biological process | en_US |
dc.subject | Evolutionary programming approach | en_US |
dc.subject | Expression levels | en_US |
dc.subject | High-quality sequences | en_US |
dc.subject | Local stochastic searches | en_US |
dc.subject | Mutation operators | en_US |
dc.subject | Secondary structures | en_US |
dc.subject | Simulated annealing techniques | en_US |
dc.subject | Bioinformatics | en_US |
dc.title | SIMARD: a simulated annealing based RNA design algorithm with quality pre-selection strategies | en_US |
dc.type | Conference Paper | en_US |
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