Efficient algorithms for model-based motif discovery from multiple sequences

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

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Detail(s)

Original languageEnglish
Title of host publicationTheory and Applications of Models of Computation
Subtitle of host publication5th International Conference, TAMC 2008, Proceedings
PublisherSpringer Verlag
Pages234-245
Volume4978 LNCS
ISBN (print)3540792279, 9783540792277
Publication statusPublished - 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4978 LNCS
ISSN (Print)0302-9743
ISSN (electronic)1611-3349

Conference

Title5th International Conference on Theory and Applications of Models of Computation, TAMC 2008
PlaceChina
CityXian
Period25 - 29 April 2008

Link(s)

DOI

DOI
Check@CityULib
Link to Scopus https://www.scopus.com/record/display.uri?eid=2-s2.0-70349303350&origin=recordpage
Permanent Link https://scholars.cityu.edu.hk/en/publications/publication(266db644-f651-449c-b4be-20e305bb092c).html

Abstract

We study a natural probabilistic model for motif discovery that has been used to experimentally test the quality of motif discovery programs. In this model, there are k background sequences, and each character in a background sequence is a random character from an alphabet ∑. A motif G=g 1 g 2...g m is a string of m characters. Each background sequence is implanted a randomly generated approximate copy of G. For a randomly generated approximate copy b 1 b 2...b m of G, every character is randomly generated such that the probability for b i ≠g i is at most α. In this paper, we give the first analytical proof that multiple background sequences do help for finding subtle and faint motifs. © 2008 Springer-Verlag Berlin Heidelberg.

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Publication fingerprints text

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Motif Discovery Keyphrases
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Multiple Sequences Keyphrases
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algorithms INIS
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approximations INIS
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Model Computer Science
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Efficient Algorithm Computer Science
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probability INIS
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randomness INIS
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probabilistic estim... INIS
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Probabilistic Model Keyphrases
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Citation Format(s)

[ RIS ] [ BibTeX ]
Efficient algorithms for model-based motif discovery from multiple sequences. / Fu, Bin; Kao, Ming-Yang; Wang, Lusheng.
Theory and Applications of Models of Computation: 5th International Conference, TAMC 2008, Proceedings. Vol. 4978 LNCS Springer Verlag, 2008. p. 234-245 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4978 LNCS).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review