Hybrid algorithms for multiple change-point detection in biological sequences

Madawa Priyadarshana*, Tatiana Polushina, Georgy Sofronov

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    3 Citations (Scopus)


    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.

    Original languageEnglish
    Pages (from-to)41-61
    Number of pages21
    JournalAdvances in Experimental Medicine and Biology
    Publication statusPublished - 2015


    • 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


    Dive into the research topics of 'Hybrid algorithms for multiple change-point detection in biological sequences'. Together they form a unique fingerprint.

    Cite this