Genetic Algorithm for Dimer-led and Error-restricted Spaced Motif Discovery
Refereed conference paper presented and published in conference proceedings


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AbstractDNA motif discovery is an important problem for deciphering protein-DNA bindings in gene regulation. To discover generic spaced motifs which have multiple conserved patterns separated by wild-cards called spacers, the genetic algorithm (GA) based GASMEN has been proposed and shown to outperform related methods. However, the over-generic modeling of any number of spacers increases the optimization difficulty in practice. In protein-DNA binding case studies, complicated spaced motifs are rare while dimers with single spacers are more common spaced motifs. Moreover, errors (mismatches) in a conserved pattern are not arbitrarily distributed as certain highly conserved nucleotides are essential to maintain bindings. Motivated by better optimization in real applications, we have developed a new method, which is GA for Dimer-led and Error-restricted Spaced Motifs (GADESM). Common spaced motifs are paid special attention to using dimer-led initialization in the population initialization. The results on real datasets show that the dimer-led initialization in GADESM achieves better fitness than GASMEN with statistical significance. With additional error-restricted motif occurrence retrieval, GADESM has shown better performance than GASMEN on both comprehensive simulation data and a real ChIP-seq case study.
All Author(s) ListChan TM, Lo LY, Wong ML, Liang Y, Leung KS
Name of Conference10th Annual IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)
Start Date of Conference16/04/2013
End Date of Conference19/04/2013
Place of ConferenceSingapore
Country/Region of ConferenceSingapore
Detailed descriptionorganized by IEEE Computational Intelligence Society,
Year2013
Month1
Day1
PublisherIEEE
Pages198 - 205
eISBN978-1-4673-5875-0
LanguagesEnglish-United Kingdom
Web of Science Subject CategoriesComputer Science; Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Mathematical & Computational Biology

Last updated on 2020-16-10 at 23:59