The Cross-Entropy method and multiple change-points detection in zero-inflated DNA read count data

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

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


    We model DNA count data as a multiple change point problem. This means that the data are divided into multiple segments based on the unknown number of change points. Each segment of the process is modeled by using a zero modified count data distribution. We observe that zeroinflated negative binomial (ZINB) model fits the data better than the competing negative binomial (NB) model. We propose an extension to the Cross-Entropy (CE) method that utilizes a beta distribution to simulate the locations of change points. Furthermore, parallel implementation of the extended CE method results a significant improvement in total processing time, in which the procedures are computationally highly intensive. We consider an artificially generated count data sequence to assess the performance of the propose method. Finally, a real DNA count data set is used to illustrate the usefulness of the proposed methodology.
    Original languageEnglish
    Title of host publicationProceedings of the 4th International Conference on Computational Methods
    Subtitle of host publicationICCM 2012
    EditorsYuanTong Gu, Suvash C. Saha
    Place of PublicationBrisbane
    PublisherQueensland University of Technology
    Number of pages8
    ISBN (Print)9781921897542
    Publication statusPublished - 2012
    EventInternational Conference on Computational Methods (4th : 2012) - Gold Coast
    Duration: 25 Nov 201228 Nov 2012


    ConferenceInternational Conference on Computational Methods (4th : 2012)
    CityGold Coast


    • Cross-Entropy method
    • change-point problem
    • combinatorial optimization
    • zero-inflated negative binomial
    • DNA count data


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