A direct optimization approach to hidden Markov modeling for single channel kinetics
A direct optimization approach to hidden Markov modeling for single channel kinetics
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DOI：
10.1016/s0006-3495(00)76441-1
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发表时间：
2000-10-01
影响因子：
3.4
通讯作者：
Sachs, F
中科院分区：
文献类型：
--
作者：
Qin, F;Auerbach, A;Sachs, F
隐马尔可夫模型（HMM）为单通道动力学建模提供了一种有效的方法。标准HMM是基于Baum的重新估计。当应用于单通道电流时，该算法无法直接优化速率常数。我们在这里提出了另一种方法，考虑作为一个一般的优化问题的问题。拟牛顿法用于搜索似然曲面。推导出似然函数的解析导数，从而最大化优化的效率。由于速率常数直接优化，该方法具有的优点，如津贴模型的约束条件和能力，同时适合在不同的实验条件下获得的多个数据集。数值例子来说明该算法的性能。与Baum的重新估计的比较表明，该方法具有上级收敛速度时，由于，例如，低信噪比或聚合的多个状态具有相同的电导的似然表面定义不佳。
Hidden Markov modeling (HMM) provides an effective approach for modeling single channel kinetics. Standard HMM is based on Baum's reestimation. As applied to single channel currents, the algorithm has the inability to optimize the rate constants directly. We present here an alternative approach by considering the problem as a general optimization problem. The quasi-Newton method is used for searching the likelihood surface. The analytical derivatives of the likelihood function are derived, thereby maximizing the efficiency of the optimization. Because the rate constants are optimized directly, the approach has advantages such as the allowance for model constraints and the ability to simultaneously fit multiple data sets obtained at different experimental conditions. Numerical examples are presented to illustrate the performance of the algorithm. Comparisons with Baum's reestimation suggest that the approach has a superior convergence speed when the likelihood surface is poorly defined due to, for example, a low signal-to-noise ratio or the aggregation of multiple states having identical conductances.