- author = "Michael A. Lones and Andy M. Tyrrell",
- title = "A Co-Evolutionary Framework for Regulatory Motif Discovery",
- booktitle = "2007 IEEE Congress on Evolutionary Computation",
- year = "2007",
- editor = "Dipti Srinivasan and Lipo Wang",
- address = "Singapore",
- month = "25-28 " # sep,
- pages = "3894--3901",
- organization = "IEEE Computational Intelligence Society",
- publisher = "IEEE Press",
- ISBN = "1-4244-1340-0",
- file = "1436.pdf",
-
DOI = "
doi:10.1109/CEC.2007.4424978",
- size = "8 pages",
- keywords = "genetic algorithms, genetic programming, biology computing, evolutionary computation, genetics, pattern classification, Boolean classification rules, co-evolutionary framework, co-expressed genes, regulatory motif discovery, sequence classifiers",
- 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.",
- homepage = "http://www-users.york.ac.uk/~mal503/",
-
notes = "also known as \cite{4424978}. Page numbers from IEEE
Xplore 2009. 3896 GP-like, binary tree.
CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET.
IEEE Catalog Number: 07TH8963C",
