Finding similar regions in many strings
STOC '99 Proceedings of the thirty-first annual ACM symposium on Theory of computing
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
FMGA: Finding Motifs by Genetic Algorithm
BIBE '04 Proceedings of the 4th IEEE Symposium on Bioinformatics and Bioengineering
Integrating Multi-Objective Genetic Algorithms into Clustering for Fuzzy Association Rules Mining
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Multi-Objective Genetic Algorithm Based Approach for Optimizing Fuzzy Sequential Patterns
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
MDGA: motif discovery using a genetic algorithm
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Multi-objective genetic algorithm based approaches for mining optimized fuzzy association rules
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Identification of weak motifs in multiple biological sequences using genetic algorithm
Proceedings of the 8th annual conference on Genetic and evolutionary computation
RISOTTO: fast extraction of motifs with mismatches
LATIN'06 Proceedings of the 7th Latin American conference on Theoretical Informatics
A fast and elitist multiobjective genetic algorithm: NSGA-II
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
MDABC: Motif Discovery Using Artificial Bee Colony Algorithm
Journal of Information Technology Research
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We propose an efficient method using multi-objective genetic algorithm (MOGAMOD) to discover optimal motifs in sequential data. The main advantage of our approach is that a large number of tradeoff (i.e., nondominated) motifs can be obtained by a single run with respect to conflicting objectives: similarity, motif length and support maximization. To the best of our knowledge, this is the first effort in this direction. MOGAMOD can be applied to any data set with a sequential character. Furthermore, it allows any choice of similarity measures for finding motifs. By analyzing the obtained optimal motifs, the decision maker can understand the tradeoff between the objectives. We compare MOGAMOD with the three well-known motif discovery methods, AlignACE, MEME and Weeder. Experimental results on real data set extracted from TRANSFAC database demonstrate that the proposed method exhibits good performance over the other methods in terms of runtime, the number of shaded samples and multiple motifs.