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首页> 外文期刊>Journal of Bioinformatics and Computational Biology >A HYBRID GENETIC ALGORITHM AND EXPECTATION MAXIMIZATION METHOD FOR GLOBAL GENE TRAJECTORY CLUSTERING
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A HYBRID GENETIC ALGORITHM AND EXPECTATION MAXIMIZATION METHOD FOR GLOBAL GENE TRAJECTORY CLUSTERING

机译：全球基因弹群聚类的混合遗传算法和期望最大化方法

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摘要

Clustering time-course gene expression data (gene trajectories) is an important step towards solving the complex problem of gene regulatory network modeling and discovery as it significantly reduces the dimensionality of the gene space required for analysis. Traditional clustering methods that perform hill-climbing from randomly initialized cluster centers are prone to produce inconsistent and sub-optimal cluster solutions over different runs. This paper introduces a novel method that hybridizes genetic algorithm (GA) and expectation maximization algorithms (EM) for clustering gene trajectories with the mixtures of multiple linear regression models (MLRs), with the objective of improving the global optimality and consistency of the clustering performance. The proposed method is applied to cluster the human fibroblasts and the yeast time-course gene expression data based on their trajectory similarities. It outperforms the standard EM method significantly in terms of both clustering accuracy and consistency. The biological implications of the improved clustering performance are demonstrated.
机译：对时程基因表达数据（基因轨迹）进行聚类是解决基因调控网络建模和发现这一复杂问题的重要步骤，因为它显着降低了分析所需的基因空间的维数。从随机初始化的聚类中心执行爬坡的传统聚类方法易于在不同的运行过程中产生不一致且次优的聚类解决方案。本文介绍了一种将遗传轨迹与多种线性回归模型（MLR）混合的遗传算法（GA）和期望最大化算法（EM）混合的新方法，目的是提高全局最优性和聚类性能的一致性。所提出的方法用于基于人类成纤维细胞和酵母时程基因表达数据的轨迹相似性进行聚类。就聚类准确性和一致性而言，它明显优于标准EM方法。证明了改进的聚类性能的生物学意义。

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