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A practical method for approximate subsequence search in DNA databases

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dc.contributor.authorWon, Jung Im-
dc.contributor.authorHong, Sang Kyoon-
dc.contributor.authorYoon, Jee Hee-
dc.contributor.authorPark, Sanghyun-
dc.contributor.authorKim, Sang Wook-
dc.date.accessioned2022-12-21T08:13:55Z-
dc.date.available2022-12-21T08:13:55Z-
dc.date.created2022-09-16-
dc.date.issued2007-05-
dc.identifier.issn0302-9743-
dc.identifier.urihttps://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/180122-
dc.description.abstractIn this paper, we propose an accurate and efficient method for approximate subsequence search in large DNA databases. The proposed method basically adopts a binary trie as its primary structure and stores all the window subsequences extracted from a DNA sequence. For approximate subsequence search, it traverses the binary trie in a breadth-first fashion and retrieves all the matched subsequences from the traversed path within the trie by a dynamic programming technique. However, the proposed method stores only window subsequences of the pre-determined length, and thus suffers from large post-processing time in case of long query sequences. To overcome this problem, we divide a query sequence into shorter pieces, perform searching for those subsequences, and then merge their results.-
dc.language영어-
dc.language.isoen-
dc.publisherSpringer Verlag-
dc.titleA practical method for approximate subsequence search in DNA databases-
dc.typeArticle-
dc.contributor.affiliatedAuthorWon, Jung Im-
dc.contributor.affiliatedAuthorKim, Sang Wook-
dc.identifier.doi10.1007/978-3-540-71701-0_103-
dc.identifier.scopusid2-s2.0-38049171347-
dc.identifier.bibliographicCitationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v.4426 LNAI, pp.921 - 931-
dc.relation.isPartOfLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)-
dc.citation.titleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)-
dc.citation.volume4426 LNAI-
dc.citation.startPage921-
dc.citation.endPage931-
dc.type.rimsART-
dc.type.docTypeConference Paper-
dc.description.journalClass1-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscopus-
dc.subject.keywordPlusApproximation theory-
dc.subject.keywordPlusBinary trees-
dc.subject.keywordPlusDatabase systems-
dc.subject.keywordPlusDynamic programming-
dc.subject.keywordPlusProblem solving-
dc.subject.keywordPlusQuery processing-
dc.subject.keywordPlusApproximate subsequence search-
dc.subject.keywordPlusQuery sequences-
dc.subject.keywordPlusSuffix trees-
dc.subject.keywordPlusDNA sequences-
dc.subject.keywordAuthorApproximate subsequence search-
dc.subject.keywordAuthorDNA database-
dc.subject.keywordAuthorSuffix tree-
dc.identifier.urlhttps://link.springer.com/chapter/10.1007/978-3-540-71701-0_103-
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서울 공과대학 > 서울 컴퓨터소프트웨어학부 > 1. Journal Articles
서울 공과대학 > 서울 공학교육혁신센터 > 1. Journal Articles

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