Hidden Markov models for sequence analysis: extension and analysis of the basic method
Hidden Markov models for sequence analysis: extension and analysis of the basic method
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DOI：
10.1093/bioinformatics/12.2.95
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发表时间：
1996-04
期刊：
影响因子：
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通讯作者：
R. Hughey;A. Krogh
中科院分区：
文献类型：
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作者：
R. Hughey;A. Krogh
隐马尔可夫模型(HMM)是对一组未比对序列或一组未比对序列中的公共基序进行建模的一种非常有效的方法。然后，训练好的HMM可用于区分或多个比对。隐马尔可夫模型的基本数学描述及其期望最大化训练过程相对简单。在本文中，我们回顾了将该方法从理论推向实践的数学扩展和启发式方法。然后对模型正则化、动态模型修正和优化策略的有效性进行了实验分析。最后，在SH2结构域上演示了如何使用特殊的模型类型从未比对的序列中找到结构域。实验工作是在序列比对和建模软件套件的帮助下完成的。
Hidden Markov models (HMMs) are a highly effective means of modeling a family of unaligned sequences or a common motif within a set of unaligned sequences. The trained HMM can then be used for discrimination or multiple alignment. The basic mathematical description of an HMM and its expectation-maximization training procedure is relatively straightforward. In this paper, we review the mathematical extensions and heuristics that move the method from the theoretical to the practical. We then experimentally analyze the effectiveness of model regularization, dynamic model modification and optimization strategies. Finally it is demonstrated on the SH2 domain how a domain can be found from unaligned sequences using a special model type. The experimental work was completed with the aid of the Sequence Alignment and Modeling software suite.