摘要翻译：
在过去的几十年里，人们对DNA片段和序列进行了深入的研究。计算生物学的主要问题之一是利用数学技术识别基因内的外显子-内含子结构。以前的研究使用了不同的方法，如傅立叶分析和隐马尔可夫模型，以便能够预测基因的哪些部分对应于蛋白质编码区。本文将半马尔可夫模型应用于表征基因蛋白质编码区的3碱基周期序列。给出了相关概率和相应指标的解析形式，从而得到了对潜在周期模式的描述。最后，用合成数据和实际数据的DNA序列对前面的理论结果进行了验证。
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英文标题：
《Investigating some attributes of periodicity in DNA sequences via
  semi-Markov modelling》
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作者：
Pavlos Kolias and Alexandra Papadopoulou
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最新提交年份：
2019
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分类信息：

一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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一级分类：Quantitative Biology        数量生物学
二级分类：Other Quantitative Biology        其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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英文摘要：
  DNA segments and sequences have been studied thoroughly during the past decades. One of the main problems in computational biology is the identification of exon-intron structures inside genes using mathematical techniques. Previous studies have used different methods, such as Fourier analysis and hidden-Markov models, in order to be able to predict which parts of a gene correspond to a protein encoding area. In this paper, a semi-Markov model is applied to 3-base periodic sequences, which characterize the protein-coding regions of the gene. Analytic forms of the related probabilities and the corresponding indexes are provided, which yield a description of the underlying periodic pattern. Last, the previous theoretical results are illustrated with DNA sequences of synthetic and real data.
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PDF链接：
https://arxiv.org/pdf/1907.03119