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Signal and Image Analysis for Biomedical and Life Sciences pp 41–61Cite as

Hybrid Algorithms for Multiple Change-Point Detection in Biological Sequences

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Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 823))

Abstract

Array comparative genomic hybridization (aCGH) is one of the techniques that can be used to detect copy number variations in DNA sequences in high resolution. It has been identified that abrupt changes in the human genome play a vital role in the progression and development of many complex diseases. In this study we propose two distinct hybrid algorithms that combine efficient sequential change-point detection procedures (the Shiryaev-Roberts procedure and the cumulative sum control chart (CUSUM) procedure) with the Cross-Entropy method, which is an evolutionary stochastic optimization technique to estimate both the number of change-points and their corresponding locations in aCGH data. The proposed hybrid algorithms are applied to both artificially generated data and real aCGH experimental data to illustrate their usefulness. Our results show that the proposed methodologies are effective in detecting multiple change-points in biological sequences of continuous measurements.

This work was carried out when the author was at the Department of Mathematics, Mari State University, Russia

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Acknowledgements

W. J. R. M. Priyadarshana acknowledges the funding received towards his PhD from the International Macquarie University Research Excellence (iMQRES) scholarship. The authors acknowledge the anonymous referees for their useful comments.

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Authors and Affiliations

  1. Faculty of Science, Department of Statistics, Macquarie University, Sydney, NSW, 2109, Australia

    Madawa Priyadarshana & Georgy Sofronov

  2. Faculty of Medicine and Dentistry, Department of Clinical Science, University of Bergen, 7804, NO-5020, Bergen, Norway

    Tatiana Polushina

Authors
  1. Madawa Priyadarshana
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  2. Tatiana Polushina
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  3. Georgy Sofronov
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Corresponding author

Correspondence to Madawa Priyadarshana .

Editor information

Editors and Affiliations

  1. Digital Productivity Flagship, CSIRO, Sydney, New South Wales, Australia

    Changming Sun

  2. Digital Productivity Flagship, CSIRO, Sydney, New South Wales, Australia

    Tomasz Bednarz

  3. The University of Aizu, Fukushima, Japan

    Tuan D. Pham

  4. Digital Productivity Flagship, CSIRO, Sydney, New South Wales, Australia

    Pascal Vallotton

  5. Digital Productivity Flagship, CSIRO, Sydney, New South Wales, Australia

    Dadong Wang

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Priyadarshana, M., Polushina, T., Sofronov, G. (2015). Hybrid Algorithms for Multiple Change-Point Detection in Biological Sequences. In: Sun, C., Bednarz, T., Pham, T., Vallotton, P., Wang, D. (eds) Signal and Image Analysis for Biomedical and Life Sciences. Advances in Experimental Medicine and Biology, vol 823. Springer, Cham. https://doi.org/10.1007/978-3-319-10984-8_3

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