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.
Original language | English |
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Title of host publication | 2007 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-10, PROCEEDINGS |
Place of Publication | NEW YORK |
Publisher | IEEE |
Pages | 3894-3901 |
Number of pages | 8 |
ISBN (Print) | 978-1-4244-1339-3 |
Publication status | Published - 2007 |
Event | IEEE Congress on Evolutionary Computation - Singapore Duration: 25 Sept 2007 → 28 Sept 2007 |
Conference
Conference | IEEE Congress on Evolutionary Computation |
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City | Singapore |
Period | 25/09/07 → 28/09/07 |
Keywords
- TRANSCRIPTIONAL REGULATION
- SITES
- COMPUTATION
- ALGORITHMS
- EXPRESSION
- PROMOTERS
- PROFILES
- SEQUENCE
- MODULES