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首页> 外文会议>International Workshop on Algorithms in Bioinformatics(WABI 2004); 20040917-21; Bergen(NO) >The Most Probable Labeling Problem in HMMs and Its Application to Bioinformatics
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The Most Probable Labeling Problem in HMMs and Its Application to Bioinformatics

机译：HMM中最可能的标记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 element is represented by states with the same label. A sequence should be annotated with the labeling of highest probability. Computing this most probable labeling was shown NP-hard by Lyngso and Pedersen. We improve this result by proving the problem NP-hard for a fixed HMM. High probability labelings are often found by heuristics, such as taking the labeling corresponding to the most probable state path. We introduce an efficient algorithm that computes the most probable labeling for a wide class of HMMs, including models previously used for transmembrane protein topology prediction and coding region detection.
机译：隐马尔可夫模型（HMM）通常用于生物序列注释。每个序列元素都由带有相同标签的状态表示。序列应标注最高概率。 Lyngso和Pedersen将计算最可能的标记显示为NP-hard。我们通过证明固定HMM的NP-难问题来改善此结果。启发式方法通常会发现高概率标签，例如采用与最可能的状态路径相对应的标签。我们介绍了一种有效的算法，该算法可以为多种HMM计算最可能的标记，包括先前用于跨膜蛋白拓扑预测和编码区检测的模型。

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