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Classification of leukemia gene expression profiles based on multivariant optimization algorithm

机译:基于多变量优化算法的白血病基因表达谱分类

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Classification of leukemia samples based on gene expression profiles has been proved an efficient way. Large numbers of intelligence algorithms have been exploited based on this purpose. However, few of them display stable and accurate performance for both low and high gene dimensionalities. Still none of them could keep the history information of optimization. Here, a classification algorithm based on the novel multivariant optimization algorithm (MOA) is proposed. Leukemia gene expression profiles with different dimensionalities are used for validation. The particle swarm optimization (PSO) and the two-layer particle swarm optimization (TLPSO) algorithm are used for comparison. The MOA shows stable and relatively accurate classification performance and could be used as an effective classification algorithm for gene expression profiles.
机译:已经证明基于基因表达谱对白血病样品进行分类是一种有效的方法。基于此目的,已经开发了许多智能算法。但是,它们中很少有人对低和高基因维度显示稳定和准确的性能。它们仍然无法保留优化的历史信息。在此,提出了一种基于新颖的多元优化算法(MOA)的分类算法。具有不同维度的白血病基因表达谱用于验证。比较了粒子群优化算法(PSO)和两层粒子群优化算法(TLPSO)。 MOA显示稳定且相对准确的分类性能,可以用作基因表达谱的有效分类算法。

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