Elements of information theory
Elements of information theory
Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
The evolutionary computation approach to motif discovery in biological sequences
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Identification of weak motifs in multiple biological sequences using genetic algorithm
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Bioinformatics
Regulatory Motif Discovery Using a Population Clustering Evolutionary Algorithm
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Bioinformatics
Motif discovery using multi-objective genetic algorithm in biosequences
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
Challenges rising from learning motif evaluation functions using genetic programming
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Speeding up exact motif discovery by bounding the expected clump size
WABI'10 Proceedings of the 10th international conference on Algorithms in bioinformatics
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The motif discovery problem consists of finding over-represented patterns in a collection of sequences. Its difficulty stems partly from the large number of possibilities to define both the motif space to be searched and the notion of over-representation. Since the size of the search space is generally exponential in the motif length, many heuristic methods, including evolutionary algorithms, have been developed. However, comparatively little attention has been devoted to the adequate evaluation of motif quality, especially when comparing motifs of different lengths. We propose an evolution strategy to solve the motif discovery problem based on a new fitness function that simultaneously takes into account (1) the number of motif occurrences, (2) the motif length, and (3) its information content. Experimental results show that the proposed method succeeds in uncovering the correct motif positions and length with high accuracy.