Hidden markov analysis of short single molecule intensity trajectories.
Hidden markov analysis of short single molecule intensity trajectories.
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DOI：
10.1021/jp907019p
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发表时间：
2009-10-22
期刊：
影响因子：
--
通讯作者：
Dickson RM
中科院分区：
文献类型：
--
作者：
Jung S;Dickson RM
单分子实验的光子轨迹可以报告生物分子的结构变化和运动。隐马尔可夫模型（HMM）有助于通过构建概率模型从噪声数据中提取隐藏状态序列。通常，真实的状态数由贝叶斯信息准则 (BIC) 确定，但是，短数据集和荧光等辐射过程中的泊松分布光子所产生的约束可能会限制拟合优度统计的成功应用。对于单分子强度轨迹，峰值定位误差 (LE) 和卡方概率等附加信息标准可以在修改正常 HMM 的同时纳入对实验数据的理论约束。卡方最小化还充当训练系统参数的迭代的停止点。 Peak LE 能够排除过度拟合和重叠状态。这些约束和标准在模拟单分子轨迹上针对 BIC 进行测试，以最好地识别任何序列中发射水平的真实数量。
Photon trajectories from single molecule experiments can report on biomolecule structural changes and motions. Hidden Markov models (HMM) facilitate extraction of the sequence of hidden states from noisy data through construction of probabilistic models. Typically, the true number of states is determined by the Bayesian information criteria (BIC), however, constraints resulting from short data sets and Poisson-distributed photons in radiative processes like fluorescence can limit successful application of goodness-of-fit statistics. For single molecule intensity trajectories, additional information criteria such as peak localization error (LE) and chi-square probabilities can incorporate theoretical constraints on experimental data while modifying normal HMM. Chi-square minimization also serves as a stopping point of the iteration in which the system parameters are trained. Peak LE enables exclusion of overfitted and overlapped states. These constraints and criteria are tested against BIC on simulated single molecule trajectories to best identify the true number of emissive levels in any sequence.
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