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An Evolutionary Monte Carlo algorithm for identifying short adjacent repeats in multiple sequences

机译:一种用于识别多个序列中短相邻重复序列的进化蒙特卡洛算法

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Evolutionary Monte Carlo (EMC) algorithm is an effective and powerful method to sample complicated distributions. Short adjacent repeats identification problem (SARIP), i.e., searching for the common sequence pattern in multiple DNA sequences, is considered as one of the key challenges in the field of bioinformatics. A recently proposed Markov chain Monte Carlo (MCMC) algorithm has demonstrated its effectiveness in solving SARIP. However, high computation time and inevitable local optima hinder its wide application. In this paper, we apply EMC to parallelize the MCMC algorithm to solve SARIP. Our proposed EMC scheme is implemented on a parallel platform and the simulation results show that, compared with the conventional MCMC algorithm, EMC not only improves the quality of final solution but also reduces the computation time.
机译:进化蒙特卡罗(EMC)算法是一种样本复杂分布的有效和强大的方法。短相邻的重复识别问题(Sarip),即,搜索多种DNA序列中的常见序列模式,被认为是生物信息学领域的关键挑战之一。最近提出的Markov Chain Monte Carlo(MCMC)算法已经证明其在求解Sarip方面的有效性。但是,高计算时间和不可避免的本地Optima阻碍了其广泛的应用。在本文中,我们应用EMC并将MCMC算法并行化以解决Sarip。我们所提出的EMC计划在并行平台上实施,仿真结果表明,与传统的MCMC算法相比,EMC不仅可以提高最终解决方案的质量,而且还降低了计算时间。

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