A hybrid genetic algorithm for change-point detection in binary biomolecular sequences

Tatiana V. Polushina*, Georgy Yu Sofronov

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

6 Citations (Scopus)

Abstract

Genomes of eukaryotic organisms vary in GC ratio, that is, share of DNA bases such that C or G as contrary to T or A. Statistical identification of segments that are internally homogenous with respect to GC ratio is essential for understanding of evolutionary processes and the different functional characteristics of the genome. It appears that DNA segmentation concerns one of the most important applications involving change-point detection. Problems of this type arise in various areas, such as speech and image processing, biomedical applications, econometrics, industry and seismology. In this study, we develop a hybrid genetic algorithm for detecting change-points in binary sequences. We apply our algorithm to both synthetic and real data sets, and demonstrate that it is more effective than other well-known methods such as Markov chain Monte Carlo, Cross-Entropy and Genetic algorithms.

Original languageEnglish
Title of host publicationIASTED Multiconferences - Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2013
EditorsE. P. Klement, W. Borutzky, T. Fahringer, M. H. Hamza, V. Uskov
Place of PublicationCalgary, AB
PublisherACTA Press
Pages1-8
Number of pages8
ISBN (Print)9780889869431
DOIs
Publication statusPublished - 2013
Event12th IASTED International Conference on Artificial Intelligence and Applications, AIA 2013 - Innsbruck, Austria
Duration: 11 Feb 201313 Feb 2013

Other

Other12th IASTED International Conference on Artificial Intelligence and Applications, AIA 2013
CountryAustria
CityInnsbruck
Period11/02/1313/02/13

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  • Cite this

    Polushina, T. V., & Sofronov, G. Y. (2013). A hybrid genetic algorithm for change-point detection in binary biomolecular sequences. In E. P. Klement, W. Borutzky, T. Fahringer, M. H. Hamza, & V. Uskov (Eds.), IASTED Multiconferences - Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2013 (pp. 1-8). Calgary, AB: ACTA Press. https://doi.org/10.2316/P.2013.793-026