Discover Gene Specific Local Co-regulations Using Progressive Genetic Algorithm

Ji Zhang, Q. Gao, Hai H. Wang
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引用次数: 3

Abstract

The problem of gene specific co-regulation discovery is that, for a particular gene of interest, identify its closely coregulated genes and the associated subsets of experimental conditions in which such co-regulations occur. The coregulations are local in the sense that they occur in some subsets of full experimental conditions. In this paper, we propose an innovative method for finding gene specific coregulations using genetic algorithm (GA). Two novel ad hoc GAs, the single-stage and two-stage progressive GA, are proposed. They are called progressive because the initial population for the GA in a window position inherits the top-ranked individuals obtained in the preceding window position, enabling them to achieve better accuracy than the nonprogressive algorithm. Experimental results with real-life gene expression data demonstrate the efficiency and effectiveness of our technique in discovering gene specific coregulations
利用渐进式遗传算法发现基因特异性局部协同调控
基因特异性共调控发现的问题是,对于感兴趣的特定基因,确定其密切共调控的基因以及发生这种共调控的相关实验条件子集。从某种意义上说,这些协规是局部的,它们发生在完整实验条件的某些子集中。在本文中,我们提出了一种利用遗传算法(GA)寻找基因特异性共调控的创新方法。提出了两种新的自适应遗传算法:单级遗传算法和两级渐进遗传算法。它们被称为渐进的,因为在一个窗口位置的遗传算法的初始种群继承了在前一个窗口位置获得的排名靠前的个体,使它们比非渐进算法获得更好的精度。真实基因表达数据的实验结果证明了我们的技术在发现基因特异性共调控方面的效率和有效性
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