Clustering Algorithms
The evolution of stochastic regular motifs for protein sequences
New Generation Computing
A Clustering Method for Improving the Global Search Capability of Genetic Algorithms
SBRN '00 Proceedings of the VI Brazilian Symposium on Neural Networks (SBRN'00)
Multiple Sequence Alignment with Evolutionary Computation
Genetic Programming and Evolvable Machines
The evolutionary computation approach to motif discovery in biological sequences
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
A clustering based niching method for evolutionary algorithms
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Fitness sharing and niching methods revisited
IEEE Transactions on Evolutionary Computation
It's not junk!: the search for functional elements in noncoding DNA
ACM SIGEVOlution
Modeling evolutionary fitness for DNA motif discovery
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Detection of over-represented motifs corresponding to known TFBSs via motif clustering and matching
Computers & Mathematics with Applications
MotifMiner: a table driven greedy algorithm for DNA motif mining
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
A Cluster Refinement Algorithm for Motif Discovery
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Hybrid multiobjective artificial bee colony with differential evolution applied to motif finding
EvoBIO'13 Proceedings of the 11th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Designing a novel hybrid swarm based multiobjective evolutionary algorithm for finding DNA motifs
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Journal of Global Optimization
A parallel cooperative team of multiobjective evolutionary algorithms for motif discovery
The Journal of Supercomputing
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This paper describes a novel evolutionary algorithm for regulatory motif discovery in DNA promoter sequences. The algorithm uses data clustering to logically distribute the evolving population across the search space. Mating then takes place within local regions of the population, promoting overall solution diversity and encouraging discovery of multiple solutions. Experiments using synthetic data sets have demonstrated the algorithm's capacity to find position frequency matrix models of known regulatory motifs in relatively long promoter sequences. These experiments have also shown the algorithm's ability to maintain diversity during search and discover multiple motifs within a single population. The utility of the algorithm for discovering motifs in real biological data is demonstrated by its ability to find meaningful motifs within muscle-specific regulatory sequences.