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Preliminary Results for GAMI: A Genetic Algorithms Approach to Motif Inference

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7 Author(s)
Congdon, C.B. ; Department of Computer Science, Colby College, Waterville, ME, 04901, Email: ccongdon@colby.edu ; Fizer, C.W. ; Smith, N.W. ; Gaskins, H.R.
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We have developed GAMI, an approach to motif inference that uses a genetic algorithms search and is designed specifically to work with divergent species and possibly long nucleotide sequences. The system design reduces the size of the search space as compared to typical window-location approaches for motif inference. This paper describes the motivation and system design for GAMI, discusses how we have designed the search space and compares this to the search space of other approaches, and presents initial results with data from the literature and from novel tasks. GAMI is able to find a host of putative conserved patterns; possible approaches for validating the utility of the conserved regions are discussed.

Published in:

Computational Intelligence in Bioinformatics and Computational Biology, 2005. CIBCB '05. Proceedings of the 2005 IEEE Symposium on

Date of Conference:

14-15 Nov. 2005

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