RECOMB '04 Proceedings of the eighth annual international conference on Resaerch in computational 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
An Efficient Algorithm for the Identification of Structured Motifs in DNA Promoter Sequences
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Identification of weak motifs in multiple biological sequences using genetic algorithm
Proceedings of the 8th annual conference on Genetic and evolutionary computation
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A Novel Approach to Extract Structured Motifs by Multi-Objective Genetic Algorithm
CBMS '08 Proceedings of the 2008 21st IEEE International Symposium on Computer-Based Medical Systems
MOGAMOD: Multi-objective genetic algorithm for motif discovery
Expert Systems with Applications: An International Journal
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
| Hi-index | 12.05 |
A structured motif is defined as a collection of highly conserved simple motifs with pre-specified sizes and gaps between them. In structured motif extraction, while all simple motifs are unknown, all gap ranges are known earlier. In this paper, we propose a novel method using multi-objective evolutionary algorithm to extract automatically extended structured motifs in which all simple motifs and gap ranges are unknown. The method employs three conflicting objectives; similarity and support maximization and total gap range minimization. To the best of our knowledge, this is the first effort in this direction. The proposed method can be applied to any data set with a sequential character. Furthermore, it allows any choice of similarity measures for finding motifs. We compare our method with the two well-known structured motif extraction methods, EXMOTIF and RISOTTO. Experiments conducted on synthetics and real data set demonstrate that the proposed method exhibits good performance over the other methods in terms of runtime and accuracy.