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Improved Approach for Haplotype Inference Based on Markov Chain

机译:基于马尔可夫链的单倍型推理的改进方法

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Variable-order Markov model (VMM) is an important statistical method for haplotype inference problem. It is well-suited for sparse marker maps and large-scale data. The existing algorithm, HaploRec, solves VMM by a greedy algorithm with pruning strategy. We present an improved Expectation-Maximization (EM) algorithm for VMM, which is based on dynamic programming (DP). The computational experimental results with simulated and real data show that the proposed algorithm can greatly improve the accuracy of VMM with an acceptable running time.The methods described in this paper are implemented in a software package, HMC, which is available from the internet.
机译:变阶马尔可夫模型(VMM)是单倍型推断问题的重要统计方法。它非常适合稀疏标记图和大规模数据。现有的算法HaploRec通过带有修剪策略的贪婪算法来求解VMM。我们为VMM提出了一种改进的期望最大化(EM)算法,该算法基于动态编程(DP)。模拟和真实数据的计算实验结果表明,所提出的算法可以在可接受的运行时间下极大地提高VMM的精度。本文描述的方法在HMC软件包中实现,可从Internet上获得。

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