DNA序列的二阶隐马尔科夫模型分类
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国家自然科学基金(61175123)


Second-Order Hidden Markov Model for DNA Sequence Classification
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    摘要:

    隐马尔可夫模型是对DNA序列建模的一种简单且有效的模型, 实际应用中通常采用一阶隐马尔可夫模型. 然而, 由于其一阶无后效性的特点, 一阶隐马尔科夫模型无法表示非相邻碱基间的依赖关系, 从而导致序列中一些有用统计特征的丢失. 本文在分析DNA序列特有的生物学构造的基础上, 提出一种用于DNA序列分类的二阶隐马尔可夫模型, 该模型继承了一阶隐马尔可夫模型的优点, 充分表达了蕴涵在DNA序列中的生物学统计特征, 使得新模型具有明确的生物学意义. 基于新模型, 提出一种DNA序列的贝叶斯分类新方法, 并在实际DNA序列上进行了实验验证. 实验结果表明, 由于二阶隐马尔可夫模型充分反映了DNA序列碱基间的结构信息, 新方法有效地提高了序列的分类精度.

    Abstract:

    Hidden Markov Model (HMM) is one of the simple but effective models for DNA sequence modeling, and the first-order HMM has been popularly used in practice. However, due to the non-aftereffect property, a first-order HMM cannot describe the dependencies between adjacent bases. This generally results in the loss of useful statistics in sequences. In this paper, based on the analysis of the specific biological structure for DNA sequences, a second-order HMM for DNA sequence classification is proposed. The new model inherits the advantages of the first-order model, while fully expresses the biological statistics contained in the DNA sequences, which makes the model more meaningful in biology. Based on the new model, a new Bayesian method is proposed for DNA sequence classification, which is experimentally evaluated on the real DNA sequences. The experimental results show that the new method is able to obtain high classification accuracy, as the structure information hidden in bases of DNA sequences can be captured more adequately by the new second-order HMM.

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郭彦明,陈黎飞,郭躬德. DNA序列的二阶隐马尔科夫模型分类.计算机系统应用,2015,24(9):22-28

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  • 收稿日期:2014-12-25
  • 最后修改日期:2015-03-18
  • 在线发布日期: 2015-09-14
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