Unsupervised Learning of Multiple Motifs in Biopolymers Using Expectation Maximization
Machine Learning - Special issue on applications in molecular biology
Computers and Operations Research
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Journal of Global Optimization
FMGA: Finding Motifs by Genetic Algorithm
BIBE '04 Proceedings of the 4th IEEE Symposium on Bioinformatics and Bioengineering
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
Identification of weak motifs in multiple biological sequences using genetic algorithm
Proceedings of the 8th annual conference on Genetic and evolutionary computation
On the performance of artificial bee colony (ABC) algorithm
Applied Soft Computing
Regulatory Motif Discovery Using a Population Clustering Evolutionary Algorithm
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
MOGAMOD: Multi-objective genetic algorithm for motif discovery
Expert Systems with Applications: An International Journal
Handbook of Parametric and Nonparametric Statistical Procedures
Handbook of Parametric and Nonparametric Statistical Procedures
GSA: A Gravitational Search Algorithm
Information Sciences: an International Journal
Bacterial Foraging Optimization Algorithm Integrating Tabu Search for Motif Discovery
BIBM '09 Proceedings of the 2009 IEEE International Conference on Bioinformatics and Biomedicine
Motif Discovery Using Evolutionary Algorithms
SOCPAR '09 Proceedings of the 2009 International Conference of Soft Computing and Pattern Recognition
Firefly algorithms for multimodal optimization
SAGA'09 Proceedings of the 5th international conference on Stochastic algorithms: foundations and applications
Finding motifs in DNA sequences applying a multiobjective artificial bee colony (MOABC) algorithm
EvoBIO'11 Proceedings of the 9th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
Applying a multiobjective gravitational search algorithm (MO-GSA) to discover motifs
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Comparing multiobjective swarm intelligence metaheuristics for DNA motif discovery
Engineering Applications of Artificial Intelligence
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In this paper we analyse the scalability of seven multiobjective evolutionary algorithms when they solve large instances of a known biological problem, the motif discovery problem (MDP). The selected algorithms are a population-based and a trajectory-based algorithms (DEPT and MO-VNS, respectively), three swarm intelligence algorithms (MOABC, MO-FA, and MO-GSA), a genetic algorithm (NSGA-II), and SPEA2. The MDP is one of the most important sequence analysis problems related to discover common patterns, motifs, in DNA sequences. A motif is a nucleic acid sequence pattern that has some biological significance as being DNA binding sites for a regulatory protein, i.e., a transcription factor (TF). A biologically relevant motif must have a certain length, be found in many sequences, and present a high similarity among the subsequences which compose it. These three goals are in conflict with each other, therefore a multiobjective approach is a good way of facing the MDP. In addition, in recent years, scientists are decoding genomes of many organisms, increasing the computational workload of the algorithms. Therefore, we need algorithms that are able to deal with these new large DNA instances. The obtained experimental results suggest that MOABC and MO-FA are the algorithms with the best scalability behaviours.