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

Tatiana V. Polushina; Georgy Yu Sofronov

doi:10.2316/P.2013.793-026

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

Tatiana V. Polushina^*, Georgy Yu Sofronov

^*Corresponding author for this work

Centre for Risk Analytics

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

8 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 language	English
Title of host publication	IASTED Multiconferences - Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2013
Editors	E. P. Klement, W. Borutzky, T. Fahringer, M. H. Hamza, V. Uskov
Place of Publication	Calgary, AB
Publisher	ACTA Press
Pages	1-8
Number of pages	8
ISBN (Print)	9780889869431
DOIs	https://doi.org/10.2316/P.2013.793-026
Publication status	Published - 2013
Event	12th IASTED International Conference on Artificial Intelligence and Applications, AIA 2013 - Innsbruck, Austria Duration: 11 Feb 2013 → 13 Feb 2013

Other

Other	12th IASTED International Conference on Artificial Intelligence and Applications, AIA 2013
Country/Territory	Austria
City	Innsbruck
Period	11/02/13 → 13/02/13

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10.2316/P.2013.793-026

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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). ACTA Press. https://doi.org/10.2316/P.2013.793-026

Polushina, Tatiana V. ; Sofronov, Georgy Yu. / A hybrid genetic algorithm for change-point detection in binary biomolecular sequences. IASTED Multiconferences - Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2013. editor / E. P. Klement ; W. Borutzky ; T. Fahringer ; M. H. Hamza ; V. Uskov. Calgary, AB : ACTA Press, 2013. pp. 1-8

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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.",

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Polushina, TV & Sofronov, GY 2013, A hybrid genetic algorithm for change-point detection in binary biomolecular sequences. in EP Klement, W Borutzky, T Fahringer, MH Hamza & V Uskov (eds), IASTED Multiconferences - Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2013. ACTA Press, Calgary, AB, pp. 1-8, 12th IASTED International Conference on Artificial Intelligence and Applications, AIA 2013, Innsbruck, Austria, 11/02/13. https://doi.org/10.2316/P.2013.793-026

A hybrid genetic algorithm for change-point detection in binary biomolecular sequences. / Polushina, Tatiana V.; Sofronov, Georgy Yu.
IASTED Multiconferences - Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2013. ed. / E. P. Klement; W. Borutzky; T. Fahringer; M. H. Hamza; V. Uskov. Calgary, AB: ACTA Press, 2013. p. 1-8.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

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AB - 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.

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Polushina TV, Sofronov GY. A hybrid genetic algorithm for change-point detection in binary biomolecular sequences. In Klement EP, Borutzky W, Fahringer T, Hamza MH, Uskov V, editors, IASTED Multiconferences - Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2013. Calgary, AB: ACTA Press. 2013. p. 1-8 doi: 10.2316/P.2013.793-026

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

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