Change-point detection in binary Markov DNA sequences by the cross-entropy method

Tatiana Polushina*, Georgy Sofronov

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

3 Citations (Scopus)

Abstract

A deoxyribonucleic acid (DNA) sequence can be represented as a sequence with 4 characters. If a particular property of the DNA is studied, for example, GC content, then it is possible to consider a binary sequence. In many cases, if the probabilistic properties of a segment differ from the neighbouring ones, this means that the segment can play a structural role. Therefore, DNA segmentation is given a special attention, and it is one of the most significant applications of change-point detection. Problems of this type also arise in a wide variety of areas, for example, seismology, industry (e.g., fault detection), biomedical signal processing, financial mathematics, speech and image processing. In this study, we have developed a Cross-Entropy algorithm for identifying change-points in binary sequences with first-order Markov dependence. We propose a statistical model for this problem and show effectiveness of our algorithm for synthetic and real datasets.

Original languageEnglish
Title of host publication2014 Federated Conference on Computer Science and Information Systems, FedCSIS 2014
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages471-478
Number of pages8
Volume2
ISBN (Electronic)9788360810583
DOIs
Publication statusPublished - 21 Oct 2014
Event2014 Federated Conference on Computer Science and Information Systems, FedCSIS 2014 - Warsaw, Poland
Duration: 7 Sep 201410 Sep 2014

Publication series

NameACSIS-Annals of Computer Science and Information Systems
PublisherIEEE
Volume2
ISSN (Print)2300-5963

Other

Other2014 Federated Conference on Computer Science and Information Systems, FedCSIS 2014
CountryPoland
CityWarsaw
Period7/09/1410/09/14

Keywords

  • ISOCHORE CHROMOSOME MAPS
  • EUKARYOTIC GENOMES
  • GC-CONTENT
  • IDENTIFICATION
  • SEGMENTATION
  • OPTIMIZATION
  • EXPRESSION
  • CRITERION
  • SAMPLER
  • DOMAINS

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