Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
DRESS: dimensionality reduction for efficient sequence search
Vise andre og tillknytning
Rekke forfattare: 5
2015 (engelsk)Inngår i: Data mining and knowledge discovery, ISSN 1384-5810, E-ISSN 1573-756X, Vol. 29, nr 5, 1280-1311 s.Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Similarity search in large sequence databases is a problem ubiquitous in a wide range of application domains, including searching biological sequences. In this paper we focus on protein and DNA data, and we propose a novel approximate method method for speeding up range queries under the edit distance. Our method works in a filter-and-refine manner, and its key novelty is a query-sensitive mapping that transforms the original string space to a new string space of reduced dimensionality. Specifically, it first identifies the most frequent codewords in the query, and then uses these codewords to convert both the query and the database to a more compact representation. This is achieved by replacing every occurrence of each codeword with a new letter and by removing the remaining parts of the strings. Using this new representation, our method identifies a set of candidate matches that are likely to satisfy the range query, and finally refines these candidates in the original space. The main advantage of our method, compared to alternative methods for whole sequence matching under the edit distance, is that it does not require any training to create the mapping, and it can handle large query lengths with negligible losses in accuracy. Our experimental evaluation demonstrates that, for higher range values and large query sizes, our method produces significantly lower costs and runtimes compared to two state-of-the-art competitor methods.

sted, utgiver, år, opplag, sider
2015. Vol. 29, nr 5, 1280-1311 s.
Emneord [en]
Similarity search, Alphabet reduction, Biological sequences
HSV kategori
Identifikatorer
URN: urn:nbn:se:su:diva-120892DOI: 10.1007/s10618-015-0413-2ISI: 000359996900008OAI: oai:DiVA.org:su-120892DiVA: diva2:860541
Tilgjengelig fra: 2015-10-12 Laget: 2015-09-18 Sist oppdatert: 2015-10-12bibliografisk kontrollert

Open Access i DiVA

Fulltekst mangler

Andre lenker

Forlagets fulltekst

Søk i DiVA

Av forfatter/redaktør
Papapetrou, Panagiotis
Av organisasjonen
I samme tidsskrift
Data mining and knowledge discovery

Søk utenfor DiVA

GoogleGoogle Scholar

Altmetric

Totalt: 17 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf