截断轮廓隐马尔可夫模型

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI:10.1109/CIBCB.2005.1594926

Scott F. Smith

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引用次数: 1

摘要

隐马尔可夫模型(HMM)是远程同源数据库搜索的一种有效方法。然而，根据概要HMM评估每个数据库序列的分数是计算要求很高的。分数评估所需的计算时间与轮廓HMM中的状态数成正比。本文研究了状态数是否可以被截断而不降低HMM寻找含有蛋白质结构域家族成员的蛋白质的能力。提出了一种寻找HMM状态截断的遗传算法(GA)。使用截断搜索酵母，大肠杆菌和猪基因组的几个不同的蛋白质结构域家族的结果显示。

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Truncated Profile Hidden Markov Models

The profile hidden Markov model (HMM) is a powerful method for remote homolog database search. However, evaluating the score of each database sequence against a profile HMM is computationally demanding. The computation time required for score evaluation is proportional to the number of states in the profile HMM. This paper examines whether the number of states can be truncated without reducing the ability of the HMM to find proteins containing members of a protein domain family. A genetic algorithm (GA) is presented which finds a good truncation of the HMM states. The results of using truncation on searches of the yeast, E. coli, and pig genomes for several different protein domain families is shown.

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2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology

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