Hughey R, Krogh A
University of California, Santa Cruz 95064, USA.
Comput Appl Biosci. 1996 Apr;12(2):95-107. doi: 10.1093/bioinformatics/12.2.95.
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.
隐马尔可夫模型（HMMs）是一种对一组未比对序列或一组未比对序列中的共同基序进行建模的高效方法。经过训练的HMM随后可用于判别或多重比对。HMM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际应用的数学扩展和启发式方法。然后，我们通过实验分析了模型正则化、动态模型修改和优化策略的有效性。最后，在SH2结构域上展示了如何使用一种特殊的模型类型从未比对序列中找到一个结构域。实验工作借助序列比对和建模软件套件完成。
