Evolutionary computation method for pattern recognition of cis-acting sites.
This paper develops an evolutionary method that learns inductively to recognize the makeup and the position of very short consensus sequences, cis-acting sites, which are a typical feature of promoters in genomes. The method combines a Finite State Automata (FSA) and Genetic Programming (GP) to discover candidate promoter sequences in primary sequence data. An experiment measures the success of the method for promoter prediction in the human genome. This class of method can take large base pair jumps and this may enable it to process very long genomic sequences to discover gene specific cis-acting sites, and genes which are regulated together.
Howard
Daniel
D
Knowledge and Information Systems Division, QinetiQ Ltd., St. Andrews Road, Malvern, Worcestershire, UK. dhoward@qinetiq.com
Benson
Karl
K
D000465:Algorithms; D001185:Artificial Intelligence; D016384:Consensus Sequence / Q000235:genetics*; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation / Q000235:genetics*; D015894:Genome, Human; D006801:Humans; D010363:Pattern Recognition, Automated; D011401:Promoter Regions, Genetic / Q000235:genetics*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016415:Sequence Alignment / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*; D014158:Transcription, Genetic / Q000235:genetics