Future Generation Computer Systems
Combinatorial Approaches to Finding Subtle Signals in DNA Sequences
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Ant Colony Optimization
FMGA: Finding Motifs by Genetic Algorithm
BIBE '04 Proceedings of the 4th IEEE Symposium on Bioinformatics and Bioengineering
Discovering biological motifs with genetic programming
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
MDGA: motif discovery using a genetic algorithm
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
MOGAMOD: Multi-objective genetic algorithm for motif discovery
Expert Systems with Applications: An International Journal
Prediction of MHC class II binders using the ant colony search strategy
Artificial Intelligence in Medicine
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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A challenging problem in molecular biology is the identification of the specific binding sites of transcription factors in the promoter regions of genes referred to as motifs. This paper presents an Ant Colony Optimization approach that can be used to provide the motif finding problem with promising solutions. The proposed approach incorporates a modified form of the Gibbs sampling technique as a local heuristic optimization search step. Further, it searches both in the space of starting positions as well as in the space of motif patterns so that it has more chances to discover potential motifs. The approach has been implemented and tested on some datasets including the Escherichia coli CRP protein dataset. Its performance was compared with other recent proposed algorithms for finding motifs such as MEME, MotifSampler, BioProspector, and in particular Genetic Algorithms. Experimental results show that our approach could achieve comparable or better performance in terms of motif accuracy within a reasonable computational time.