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Motif-grasp and motif-ils: two new stochastic local search algorithms for motif finding

Motif-grasp and motif-ils: two new stochastic local search algorithms for motif finding
Motif-grasp and motif-ils: two new stochastic local search algorithms for motif finding
Motif finding in biosequences is a very important and well-studied problem. However, the best algorithms to date either give a poor performance on some problem instances, or are computationally impractical. As it turns out that this problem is computationally hard, in this work, we propose Motif-GRASP, a GRASP algorithm, and Motif-ILS, an ILS algorithm, to solve the motif finding problem. We have tested the two algorithms on data sets generated with the Motif-Generator we have implemented, and found that they both algorithms work pretty well, with a better performance for the GRASP algorithm. Motif-GRASP also works well for the CRP real data set, and both algorithms perform poorly for simulated sets from the Challenge Problem [9]. The results presented in this work are promising, and maybe with more improvement, we can get results comparable with state of the art algorithms for this problem.
Andronescu, Mirela
345bb228-f8cc-4bf9-bebb-00e1fefdff89
Rastegari, Baharak
6ba9e93c-53ba-4090-8f77-c1cb1568d7d1
Andronescu, Mirela
345bb228-f8cc-4bf9-bebb-00e1fefdff89
Rastegari, Baharak
6ba9e93c-53ba-4090-8f77-c1cb1568d7d1

Andronescu, Mirela and Rastegari, Baharak (2003) Motif-grasp and motif-ils: two new stochastic local search algorithms for motif finding. Mini Workshop on Stochastic Search Algorithms, Vancouver, Canada. 25 Apr 2003. 68 pp .

Record type: Conference or Workshop Item (Paper)

Abstract

Motif finding in biosequences is a very important and well-studied problem. However, the best algorithms to date either give a poor performance on some problem instances, or are computationally impractical. As it turns out that this problem is computationally hard, in this work, we propose Motif-GRASP, a GRASP algorithm, and Motif-ILS, an ILS algorithm, to solve the motif finding problem. We have tested the two algorithms on data sets generated with the Motif-Generator we have implemented, and found that they both algorithms work pretty well, with a better performance for the GRASP algorithm. Motif-GRASP also works well for the CRP real data set, and both algorithms perform poorly for simulated sets from the Challenge Problem [9]. The results presented in this work are promising, and maybe with more improvement, we can get results comparable with state of the art algorithms for this problem.

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More information

Published date: 2003
Venue - Dates: Mini Workshop on Stochastic Search Algorithms, Vancouver, Canada, 2003-04-25 - 2003-04-25

Identifiers

Local EPrints ID: 426391
URI: https://eprints.soton.ac.uk/id/eprint/426391
PURE UUID: 4543e8c6-eab7-45ff-94b5-456bd9ea9491
ORCID for Baharak Rastegari: ORCID iD orcid.org/0000-0002-0985-573X

Catalogue record

Date deposited: 27 Nov 2018 17:30
Last modified: 14 Mar 2019 01:20

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