research-article

Modeling evolutionary fitness for DNA motif discovery

Authors:

Tobias Marschall,

Oliver KramerAuthors Info & Claims

GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation

Pages 225 - 232

https://doi.org/10.1145/1569901.1569933

Published: 08 July 2009 Publication History

Abstract

The motif discovery problem consists of finding over-represented patterns in a collection of sequences. Its difficulty stems partly from the large number of possibilities to define both the motif space to be searched and the notion of over-representation. Since the size of the search space is generally exponential in the motif length, many heuristic methods, including evolutionary algorithms, have been developed. However, comparatively little attention has been devoted to the adequate evaluation of motif quality, especially when comparing motifs of different lengths. We propose an evolution strategy to solve the motif discovery problem based on a new fitness function that simultaneously takes into account (1) the number of motif occurrences, (2) the motif length, and (3) its information content. Experimental results show that the proposed method succeeds in uncovering the correct motif positions and length with high accuracy.

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Cited By

Chan TLeung KLee K(2018)Memetic Algorithms for De Novo Motif DiscoveryIEEE Transactions on Evolutionary Computation10.1109/TEVC.2011.217197216:5(730-748)Online publication date: 25-Dec-2018
https://dl.acm.org/doi/10.1109/TEVC.2011.2171972
Marschall TRahmann S(2010)Speeding up exact motif discovery by bounding the expected clump sizeProceedings of the 10th international conference on Algorithms in bioinformatics10.5555/1885783.1885818(337-349)Online publication date: 6-Sep-2010
https://dl.acm.org/doi/10.5555/1885783.1885818
Lo LChan TLee KLeung KPelikan MBranke J(2010)Challenges rising from learning motif evaluation functions using genetic programmingProceedings of the 12th annual conference on Genetic and evolutionary computation10.1145/1830483.1830515(171-178)Online publication date: 7-Jul-2010
https://dl.acm.org/doi/10.1145/1830483.1830515

Index Terms

Modeling evolutionary fitness for DNA motif discovery
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction

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cover image ACM Conferences

GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation

July 2009

2036 pages

ISBN:9781605583259

DOI:10.1145/1569901

General Chair:
Franz Rothlauf
University of Mainz, Germany

Copyright © 2009 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 08 July 2009

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GECCO09: Genetic and Evolutionary Computation Conference

July 8 - 12, 2009

Québec, Montreal, Canada

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Chan TLeung KLee K(2018)Memetic Algorithms for De Novo Motif DiscoveryIEEE Transactions on Evolutionary Computation10.1109/TEVC.2011.217197216:5(730-748)Online publication date: 25-Dec-2018
https://dl.acm.org/doi/10.1109/TEVC.2011.2171972
Marschall TRahmann S(2010)Speeding up exact motif discovery by bounding the expected clump sizeProceedings of the 10th international conference on Algorithms in bioinformatics10.5555/1885783.1885818(337-349)Online publication date: 6-Sep-2010
https://dl.acm.org/doi/10.5555/1885783.1885818
Lo LChan TLee KLeung KPelikan MBranke J(2010)Challenges rising from learning motif evaluation functions using genetic programmingProceedings of the 12th annual conference on Genetic and evolutionary computation10.1145/1830483.1830515(171-178)Online publication date: 7-Jul-2010
https://dl.acm.org/doi/10.1145/1830483.1830515

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