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The most probable annotation problem in HMMs and its application to bioinformatics

机译：HMM中最可能的注释问题及其在生物信息学中的应用

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摘要

Hidden Markov models (HMMs) are often used for biological sequence annotation. Each sequence feature is represented by a collection of states with the same label. In annotating a new sequence, we seek the sequence of labels that has highest probability. Computing this most probable annotation was shown NP-hard by Lyngso and Pedersen [R.B. Lyngso, C.N.S. Pedersen, The consensus string problem and the complexity of comparing hidden Markov models, J. Comput. System Sci. 65 (3) (2002) 545-569]. We improve their result by showing that the problem is NP-hard for a specific HMM, and present efficient algorithms to compute the most probable annotation for a large class of HMMs, including abstractions of models previously used for transmembrane protein topology prediction and coding region detection. We also present a small experiment showing that the maximum probability annotation is more accurate than the labeling that results from simpler heuristics.
机译：隐马尔可夫模型（HMM）通常用于生物序列注释。每个序列特征都由具有相同标签的状态集合表示。在注释新序列时，我们寻找具有最高概率的标记序列。 Lyngso和Pedersen [R.B.]演示了计算这种最可能的注释的NP-hard方法。林格索（C.N.S.） Pedersen，共识字符串问题和比较隐马尔可夫模型的复杂性，J。Comput。系统科学65（3）（2002）545-569]。我们通过显示问题对于特定HMM而言是NP-难问题来改善其结果，并提出了有效的算法来计算大型HMM的最可能注释，包括先前用于跨膜蛋白拓扑预测和编码区检测的模型的抽象。我们还提供了一个小型实验，显示最大概率注释比更简单的启发式方法产生的标记更准确。

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