A practical method for approximate subsequence search in DNA databases
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Won, Jung Im | - |
dc.contributor.author | Hong, Sang Kyoon | - |
dc.contributor.author | Yoon, Jee Hee | - |
dc.contributor.author | Park, Sanghyun | - |
dc.contributor.author | Kim, Sang Wook | - |
dc.date.accessioned | 2022-12-21T08:13:55Z | - |
dc.date.available | 2022-12-21T08:13:55Z | - |
dc.date.created | 2022-09-16 | - |
dc.date.issued | 2007-05 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/180122 | - |
dc.description.abstract | In 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.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Springer Verlag | - |
dc.title | A practical method for approximate subsequence search in DNA databases | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Won, Jung Im | - |
dc.contributor.affiliatedAuthor | Kim, Sang Wook | - |
dc.identifier.doi | 10.1007/978-3-540-71701-0_103 | - |
dc.identifier.scopusid | 2-s2.0-38049171347 | - |
dc.identifier.bibliographicCitation | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v.4426 LNAI, pp.921 - 931 | - |
dc.relation.isPartOf | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | - |
dc.citation.title | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | - |
dc.citation.volume | 4426 LNAI | - |
dc.citation.startPage | 921 | - |
dc.citation.endPage | 931 | - |
dc.type.rims | ART | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | Approximation theory | - |
dc.subject.keywordPlus | Binary trees | - |
dc.subject.keywordPlus | Database systems | - |
dc.subject.keywordPlus | Dynamic programming | - |
dc.subject.keywordPlus | Problem solving | - |
dc.subject.keywordPlus | Query processing | - |
dc.subject.keywordPlus | Approximate subsequence search | - |
dc.subject.keywordPlus | Query sequences | - |
dc.subject.keywordPlus | Suffix trees | - |
dc.subject.keywordPlus | DNA sequences | - |
dc.subject.keywordAuthor | Approximate subsequence search | - |
dc.subject.keywordAuthor | DNA database | - |
dc.subject.keywordAuthor | Suffix tree | - |
dc.identifier.url | https://link.springer.com/chapter/10.1007/978-3-540-71701-0_103 | - |
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