A hybrid algorithm for multiple change-point detection in continuous measurements

W. J. R. M. Priyadarshana, T. Polushina, G. Sofronov

Research output: Contribution to journalConference paperResearchpeer-review

Abstract

Array comparative genomic hybridization (aCGH) is one of the techniques that can be used to detect copy number variations in DNA sequences. It has been identified that abrupt changes in the human genome play a vital role in the progression and development of many diseases. We propose a hybrid algorithm that utilizes both the sequential techniques and the Cross-Entropy method to estimate the number of change points as well as their locations in aCGH data. We applied the proposed hybrid algorithm to both artificially generated data and real data to illustrate the usefulness of the methodology. Our results show that the proposed algorithm is an effective method to detect multiple change-points in continuous measurements.

LanguageEnglish
Pages108-117
Number of pages10
JournalAIP Conference Proceedings
Volume1559
DOIs
Publication statusPublished - 2013
Event International Symposium on Computational Models for Life Sciences
- Sydney
Duration: 27 Nov 201329 Nov 2013

Fingerprint

genome
progressions
deoxyribonucleic acid
methodology
entropy
estimates

Cite this

Priyadarshana, W. J. R. M. ; Polushina, T. ; Sofronov, G. / A hybrid algorithm for multiple change-point detection in continuous measurements. In: AIP Conference Proceedings. 2013 ; Vol. 1559. pp. 108-117.
@article{aa6fb4e746b34960bf0cc9f62ed42375,
title = "A hybrid algorithm for multiple change-point detection in continuous measurements",
abstract = "Array comparative genomic hybridization (aCGH) is one of the techniques that can be used to detect copy number variations in DNA sequences. It has been identified that abrupt changes in the human genome play a vital role in the progression and development of many diseases. We propose a hybrid algorithm that utilizes both the sequential techniques and the Cross-Entropy method to estimate the number of change points as well as their locations in aCGH data. We applied the proposed hybrid algorithm to both artificially generated data and real data to illustrate the usefulness of the methodology. Our results show that the proposed algorithm is an effective method to detect multiple change-points in continuous measurements.",
author = "Priyadarshana, {W. J. R. M.} and T. Polushina and G. Sofronov",
year = "2013",
doi = "10.1063/1.4825002",
language = "English",
volume = "1559",
pages = "108--117",
journal = "AIP Conference Proceedings",
issn = "0094-243X",
publisher = "American Institute of Physics Publising LLC",

}

A hybrid algorithm for multiple change-point detection in continuous measurements. / Priyadarshana, W. J. R. M.; Polushina, T.; Sofronov, G.

In: AIP Conference Proceedings, Vol. 1559, 2013, p. 108-117.

Research output: Contribution to journalConference paperResearchpeer-review

TY - JOUR

T1 - A hybrid algorithm for multiple change-point detection in continuous measurements

AU - Priyadarshana, W. J. R. M.

AU - Polushina, T.

AU - Sofronov, G.

PY - 2013

Y1 - 2013

N2 - Array comparative genomic hybridization (aCGH) is one of the techniques that can be used to detect copy number variations in DNA sequences. It has been identified that abrupt changes in the human genome play a vital role in the progression and development of many diseases. We propose a hybrid algorithm that utilizes both the sequential techniques and the Cross-Entropy method to estimate the number of change points as well as their locations in aCGH data. We applied the proposed hybrid algorithm to both artificially generated data and real data to illustrate the usefulness of the methodology. Our results show that the proposed algorithm is an effective method to detect multiple change-points in continuous measurements.

AB - Array comparative genomic hybridization (aCGH) is one of the techniques that can be used to detect copy number variations in DNA sequences. It has been identified that abrupt changes in the human genome play a vital role in the progression and development of many diseases. We propose a hybrid algorithm that utilizes both the sequential techniques and the Cross-Entropy method to estimate the number of change points as well as their locations in aCGH data. We applied the proposed hybrid algorithm to both artificially generated data and real data to illustrate the usefulness of the methodology. Our results show that the proposed algorithm is an effective method to detect multiple change-points in continuous measurements.

UR - http://www.scopus.com/inward/record.url?scp=84887975730&partnerID=8YFLogxK

U2 - 10.1063/1.4825002

DO - 10.1063/1.4825002

M3 - Conference paper

VL - 1559

SP - 108

EP - 117

JO - AIP Conference Proceedings

T2 - AIP Conference Proceedings

JF - AIP Conference Proceedings

SN - 0094-243X

ER -