Hybrid Algorithms for Multiple Change-Point Detection in Biological Sequences

  • Madawa PriyadarshanaEmail author
  • Tatiana Polushina
  • Georgy Sofronov
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 823)


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.


Cross-entropy method Change-point modelling aCGH data DNA sequences Copy number variation Sequential change-point analysis Shiryaev-Roberts procedure Cumulative sum procedure Combinatorial optimization Stochastic optimization 



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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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Madawa Priyadarshana
    • 1
    Email author
  • Tatiana Polushina
    • 2
  • Georgy Sofronov
    • 1
  1. 1.Faculty of Science, Department of StatisticsMacquarie UniversitySydneyAustralia
  2. 2.Faculty of Medicine and Dentistry, Department of Clinical ScienceUniversity of BergenBergenNorway

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