基于随机化的富集分析并行算法的改进

2017 International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON) Pub Date : 2017-09-01 DOI:10.1109/SIBIRCON.2017.8109886

M. V. Grishchenko, A. Yakimenko, M. Khairetdinov, A. V. Lazareva

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引用次数: 0

摘要

这项工作是由用于分析多维遗传数据的算法驱动的。这种算法被称为置换检验，被广泛用于基因集富集分析方法的一部分。本文考虑了一种排列检验算法。研究了重采样测试算法性能对输入数据的依赖关系。提出了几种改进排列检验算法的方法。实现了减少算法迭代次数的最有效方法。

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The improvement of the parallel algorithm for randomization-based enrichment analysis

This work is motivated by algorithm that is used to analyze the multidimensional genetic data. This algorithm, called permutation test, is widely used as part of gene set enrichment analysis method. In this paper, the permutation test algorithm is considered. The dependence of the resampling test algorithm performance on the input data is studied. Several ways to improve the permutation test algorithm are proposed. The most effective way consisting in the reduction of number of iterations of the algorithm is implemented.

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2017 International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON)

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