The Longest Common Exemplar Subsequence Problem

dc.contributor.authorZhang, Shu
dc.contributor.authorWang, Ruizhi
dc.contributor.authorZhu, Daming
dc.contributor.authorJiang, Haitao
dc.contributor.authorFeng, Haodi
dc.contributor.authorGuo, Jiong
dc.contributor.authorLiu, Xiaowen
dc.contributor.departmentBioHealth Informatics, School of Informatics and Computingen_US
dc.date.accessioned2019-12-23T20:16:37Z
dc.date.available2019-12-23T20:16:37Z
dc.date.issued2018-12
dc.description.abstractIn this paper, we propose to find order conserved subsequences of genomes by finding longest common exemplar subsequences of the genomes. The longest common exemplar subsequence problem is given by two genomes, asks to find a common exemplar subsequence of them, such that the exemplar subsequence length is maximized. We focus on genomes whose genes of the same gene family are in at most s spans. We propose a dynamic programming algorithm with time complexity O(s4 s mn) to find a longest common exemplar subsequence of two genomes with one genome admitting s span genes of the same gene family, where m, n stand for the gene numbers of those two given genomes. Our algorithm can be extended to find longest common exemplar subsequences of more than one genomes.en_US
dc.eprint.versionAuthor's manuscripten_US
dc.identifier.citationZhang, S., Wang, R., Zhu, D., Jiang, H., Feng, H., Guo, J., & Liu, X. (2018). The Longest Common Exemplar Subsequence Problem. 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 92–95. https://doi.org/10.1109/BIBM.2018.8621304en_US
dc.identifier.urihttps://hdl.handle.net/1805/21569
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.isversionof10.1109/BIBM.2018.8621304en_US
dc.relation.journal2018 IEEE International Conference on Bioinformatics and Biomedicineen_US
dc.rightsPublisher Policyen_US
dc.sourceAuthoren_US
dc.subjectdynamic programmingen_US
dc.subjectalgorithmen_US
dc.subjectexemplar subsequenceen_US
dc.titleThe Longest Common Exemplar Subsequence Problemen_US
dc.typeArticleen_US
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