| 隐马尔科夫过程在生物信息学中的应用 |
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| 引用本文： | 周海廷.隐马尔科夫过程在生物信息学中的应用[J].生命科学研究,2002,6(3):204-210. |
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| 作者姓名： | 周海廷 |
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| 作者单位： | 西南科技大学,生命科学与工程学院,中国四川,绵阳,621000 |
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| 摘 要： | 隐马尔科夫过程（hidden markov model,简称HMM）是20世纪70年代提出来的一种统计方法,以前主要用于语音识别。1989年Churchill将其引入计算生物学。目前,HMM是生物信息学中应用比较广泛的一种统计方法,主要用于：线性序列分析、模型分析、基因发现等方面。对HMM进行了简明扼要的描述,并对其在上述几个方面的应用作一概略介绍。
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| 关 键 词： | 隐马尔科夫过程 生物信息学 应用 序列搜索 模型估计 基因识别 |
| 文章编号： | 1007-7847(2002)03-0204-07 |
| 修稿时间： | 2001年12月18 |
An Introduction to the Hidden Markov Models for Bioinformatics |
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| ZHOU Hai-ting.An Introduction to the Hidden Markov Models for Bioinformatics[J].Life Science Research,2002,6(3):204-210. |
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| Authors: | ZHOU Hai-ting |
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| Abstract: | The Hidden Markov Model (HMM) is a statistical model, which is very well suited for many tasks in molecular biology, although they have been mostly developed for speech recognition since the early 1970's. The most popular use of the HMM in molecular biology is as a "probabilistic pro-file" of a protein family, which is called a profile HMM. From a family of proteins (or DNA) a profile HMM can be made for searching a database for other members of the family. The HMM can be applied to other types of problems. It is particularly well suited for problems with a simple "grammatical structure", such as gene finding. |
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| Keywords: | hidden Markov models sequence search model estimation gene-finding |
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