A hybrid method for protein sequence modeling with improved accuracy
Information Sciences: an International Journal
The evolution of stochastic regular motifs for protein sequences
New Generation Computing
Probabilistic Pattern Matching and the Evolution of Stochastic Regular Expressions
Applied Intelligence
On Comparing Classifiers: Pitfalls toAvoid and a Recommended Approach
Data Mining and Knowledge Discovery
Applying Boosting Techniques to Genetic Programming
Selected Papers from the 5th European Conference on Artificial Evolution
Genetic Programming and Evolvable Machines
A recursive MISD architecture for pattern matching
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Identifying structural mechanisms in standard genetic programming
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Motif finding using ant colony optimization
ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
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Choosing the right representation for a problem is important. In this article we introduce a linear genetic programming approach for motif discovery in protein families, and we also present a thorough comparison between our approach and Koza-style genetic programming using ADFs. In a study of 45 protein families, we demonstrate that our algorithm, given equal processing resources and no prior knowledge in shaping of datasets, consistently generates motifs that are of significantly better quality than those we found by using trees as representation. For several of the studied protein families we evolve motifs comparable to those found in Prosite, a manually curated database of protein motifs.Our linear genome gave better results than Koza-style genetic programming for 37 of 45 families. The difference is statistically significant for 24 of the families at the 99% confidence level.