Using Dirichlet Mixture Priors to Derive Hidden Markov Models for Protein Families
Using Dirichlet Mixture Priors to Derive Hidden Markov Models for Protein Families
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发表时间：
1993-07
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通讯作者：
Michael Brown;Comput Er Science;R. Hughey;A. Krogh;I. Mian;Kimmen Sjslander;D. Haussler
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作者：
Michael Brown;Comput Er Science;R. Hughey;A. Krogh;I. Mian;Kimmen Sjslander;D. Haussler
介绍了一种估计蛋白质家族或该家族的多列序列的隐马尔可夫模型（HMM）状态下氨基酸分布的贝叶斯方法。该方法使用狄利克雷混合物密度作为氨基酸分布的先验。这些混合物密度是通过检查先前构建的hmm或多个校准来确定的。实验表明，贝叶斯方法可以提高小训练集生成hmm的质量。在EF-hand基序的特定实验中，这些先验被证明在未见数据上产生hmm的可能性更高，并且在数据库搜索任务中产生更少的假阳性和假阴性。
A Bayesian method for estimating the amino acid distributions in the states of a hidden Markov model (HMM) for a protein family or the columns of a multiple alignment of that family is introduced. This method uses Dirichlet mixture densities as priors over amino acid distributions. These mixture densities are determined from examination of previously constructed HMMs or multiple alignments. It is shown that this Bayesian method can improve the quality of HMMs produced from small training sets. Specific experiments on the EF-hand motif are reported, for which these priors are shown to produce HMMs with higher likelihood on unseen data, and fewer false positives and false negatives in a database search task.