By the same authors

A co-evolutionary framework for regulatory motif discovery

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Publication details

Title of host publication2007 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-10, PROCEEDINGS
DatePublished - 2007
Pages3894-3901
Number of pages8
PublisherIEEE
Place of PublicationNEW YORK
Original languageEnglish
ISBN (Print)978-1-4244-1339-3

Abstract

In previous work, we have shown how an evolutionary algorithm with a clustered population can be used to concurrently discover multiple regulatory motifs present within the promoter sequences of co-expressed genes. In this paper, we extend the algorithm by co-evolving a population of Boolean classification rules in parallel with the motif population. Results using synthetic data suggest that this approach allows poorly conserved motifs to be identified in promoter sequences an order of magnitude longer than using population clustering alone, whilst results using muscle-specific promoter data show the algorithm is able to evolve meaningful sequence classifiers in parallel with motifs-suggesting that co-evolution provides a suitable framework for composite motif discovery within eukaryotic sequences.

    Research areas

  • TRANSCRIPTIONAL REGULATION, SITES, COMPUTATION, ALGORITHMS, EXPRESSION, PROMOTERS, PROFILES, SEQUENCE, MODULES

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