@inproceedings{45ec5617162b457eb9728a60cf9e0c9d,

title = "Change-point detection in binary Markov DNA sequences by the cross-entropy method",

abstract = "A deoxyribonucleic acid (DNA) sequence can be represented as a sequence with 4 characters. If a particular property of the DNA is studied, for example, GC content, then it is possible to consider a binary sequence. In many cases, if the probabilistic properties of a segment differ from the neighbouring ones, this means that the segment can play a structural role. Therefore, DNA segmentation is given a special attention, and it is one of the most significant applications of change-point detection. Problems of this type also arise in a wide variety of areas, for example, seismology, industry (e.g., fault detection), biomedical signal processing, financial mathematics, speech and image processing. In this study, we have developed a Cross-Entropy algorithm for identifying change-points in binary sequences with first-order Markov dependence. We propose a statistical model for this problem and show effectiveness of our algorithm for synthetic and real datasets.",

keywords = "ISOCHORE CHROMOSOME MAPS, EUKARYOTIC GENOMES, GC-CONTENT, IDENTIFICATION, SEGMENTATION, OPTIMIZATION, EXPRESSION, CRITERION, SAMPLER, DOMAINS",

author = "Tatiana Polushina and Georgy Sofronov",

year = "2014",

month = oct,

day = "21",

doi = "10.15439/2014F88",

language = "English",

volume = "2",

series = "ACSIS-Annals of Computer Science and Information Systems",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

pages = "471--478",

booktitle = "2014 Federated Conference on Computer Science and Information Systems, FedCSIS 2014",

address = "United States",

note = "2014 Federated Conference on Computer Science and Information Systems, FedCSIS 2014 ; Conference date: 07-09-2014 Through 10-09-2014",

}