Unsupervised Learning of Multiple Motifs in Biopolymers Using Expectation Maximization
Machine Learning - Special issue on applications in molecular biology
Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis
Artificial Intelligence Review
Finding similar regions in many strings
STOC '99 Proceedings of the thirty-first annual ACM symposium on Theory of computing
Finding motifs using random projections
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
Combinatorial Approaches to Finding Subtle Signals in DNA Sequences
Proceedings of the Eighth 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
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
A method for member selection of R&D teams using the individual and collaborative information
Expert Systems with Applications: An International Journal
Automated extraction of extended structured motifs using multi-objective genetic algorithm
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Motif finding using ant colony optimization
ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
Expert Systems with Applications: An International Journal
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
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
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
Comparing multiobjective artificial bee colony adaptations for discovering DNA motifs
EvoBIO'12 Proceedings of the 10th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
GAMIV: a genetic algorithm for identifying variable-lengthmotifs in noncoding DNA
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Comparing multiobjective swarm intelligence metaheuristics for DNA motif discovery
Engineering Applications of Artificial Intelligence
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
MDABC: Motif Discovery Using Artificial Bee Colony Algorithm
Journal of Information Technology Research
Journal of Global Optimization
A parallel cooperative team of multiobjective evolutionary algorithms for motif discovery
The Journal of Supercomputing
| Hi-index | 12.06 |
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 accuracy and runtime.