TFBS identification by position- and consensus-led genetic algorithm with local filtering
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Modeling evolutionary fitness for DNA motif discovery
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
A Monte Carlo EM Algorithm for De Novo Motif Discovery in Biomolecular Sequences
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
GAPK: genetic algorithms with prior knowledge for motif discovery in DNA sequences
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Moitf GibbsGA: Sampling Transcription Factor Binding Sites Coupled with PSFM Optimization by GA
ISICA '09 Proceedings of the 4th International Symposium on Advances in Computation and Intelligence
Multiple sequence local alignment using monte carlo EM algorithm
ISBRA'07 Proceedings of the 3rd international conference on Bioinformatics research and applications
An improved genetic algorithm for DNA motif discovery with public domain information
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Challenges rising from learning motif evaluation functions using genetic programming
Proceedings of the 12th annual conference on Genetic and evolutionary computation
A Cluster Refinement Algorithm for Motif Discovery
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
iGAPK: improved GAPK algorithm for regulatory DNA motif discovery
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
SOMIX: motifs discovery in gene regulatory sequences using self-organizing maps
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Finding gapped motifs by a novel evolutionary algorithm
EvoBIO'10 Proceedings of the 8th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Optimizing genetic algorithm for motif discovery
Mathematical and Computer Modelling: An International Journal
Memetic algorithms for de novo motif-finding in biomedical sequences
Artificial Intelligence in Medicine
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
Parallelizing a hybrid multiobjective differential evolution for identifying cis-regulatory elements
Proceedings of the 20th European MPI Users' Group Meeting
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Motivation: Identification of a transcription factor binding sites is an important aspect of the analysis of genetic regulation. Many programs have been developed for the de novo discovery of a binding motif (collection of binding sites). Recently, a scoring function formulation was derived that allows for the comparison of discovered motifs from different programs [S.T. Jensen, X.S. Liu, Q. Zhou and J.S. Liu (2004) Stat. Sci., 19, 188--204.] A simple program, BioOptimizer, was proposed in [S.T. Jensen and J.S. Liu (2004) Bioinformatics, 20, 1557--1564.] that improved discovered motifs by optimizing a scoring function. However, BioOptimizer is a very simple algorithm that can only make local improvements upon an already discovered motif and so BioOptimizer can only be used in conjunction with other motif-finding software. Results: We introduce software, GAME, which utilizes a genetic algorithm to find optimal motifs in DNA sequences. GAME evolves motifs with high fitness from a population of randomly generated starting motifs, which eliminate the reliance on additional motif-finding programs. In addition to using standard genetic operations, GAME also incorporates two additional operators that are specific to the motif discovery problem. We demonstrate the superior performance of GAME compared with MEME, BioProspector and BioOptimizer in simulation studies as well as several real data applications where we use an extended version of the GAME algorithm that allows the motif width to be unknown. Availability: http://mail.med.upenn.edu/~zhiwei/GAME/ Contact: zhiwei@mail.med.upenn.edu Supplementary information: Supplementary data are available at Bioinformatics online.