An introduction to genetic algorithms
An introduction to genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
The Jalview Java alignment editor
Bioinformatics
The evolutionary computation approach to motif discovery in biological sequences
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
It's not junk!: the search for functional elements in noncoding DNA
ACM SIGEVOlution
Modeling evolutionary fitness for DNA motif discovery
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Automated extraction of extended structured motifs using multi-objective genetic algorithm
Expert Systems with Applications: An International Journal
GAPK: genetic algorithms with prior knowledge for motif discovery in DNA sequences
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
An improved genetic algorithm for DNA motif discovery with public domain information
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
GAMIV: a genetic algorithm for identifying variable-lengthmotifs in noncoding DNA
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Initial Results In Using de Novo Motif Inference to Detect Cis-Regulatory Modules
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
Hi-index | 0.00 |
In previous work, we presented GAMI [1], an approach to motif inference that uses a genetic algorithms search. GAMI is designed specifically to find putative conserved regulatory motifs in noncoding regions of divergent species, and is designed to allow for analysis of long nucleotide sequences. In this work, we compare GAMI's performance when run with its original fitness function (a simple count of the number of matches) and when run with information content, as well as several variations on these metrics. Results indicate that information content does not identify highly conserved regions, and thus is not the appropriate metric for this task, while variations on information content as well as the original metric succeed in identifying putative conserved regions.