A co-evolutionary framework for regulatory motif discovery

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 a magnitude longer than using population clustering alone, whilst results using muscle-specific data suggest the algorithm is able to evolve meaningful sequence classifiers in parallel with motifs.

• Type: Conference
• Year: 2007
• Where: CEC
• Authors: Michael A. Lones, Andy M. Tyrrell
• Keywords: Artificial Intelligence, CEC 2007, Motif Population, Multiple Regulatory Motifs, Promoter Sequences