Maximum discrimination hidden Markov models of sequence consensus.
Maximum discrimination hidden Markov models of sequence consensus.
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DOI：
10.1089/cmb.1995.2.9
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发表时间：
1995-01-01
期刊：
影响因子：
--
通讯作者：
Durbin, R
中科院分区：
文献类型：
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作者：
Eddy, S R;Mitchison, G;Durbin, R
提出了一种用于构建蛋白质或核酸一级序列一致性的隐马尔可夫模型的最大判别方法。该方法补偿了序列数据集中的偏差表示，取代了序列加权方法的需要。当对球蛋白和蛋白激酶催化结构域序列进行检测时，HMM对远端同源序列的检测比其他各种HMM方法或BLAST方法更敏感。
We introduce a maximum discrimination method for building hidden Markov models (HMMs) of protein or nucleic acid primary sequence consensus. The method compensates for biased representation in sequence data sets, superseding the need for sequence weighting methods. Maximum discrimination HMMs are more sensitive for detecting distant sequence homologs than various other HMM methods or BLAST when tested on globin and protein kinase catalytic domain sequences.