Larsen Peter, Almasri Eyad, Chen Guanrao, Dai Yang
Core Genomics Lab., Illinois Univ., Chicago, IL 60607-7058, USA.
Conf Proc IEEE Eng Med Biol Soc. 2006;2006:5842-5. doi: 10.1109/IEMBS.2006.259256.
One of the goals of genomic expression analysis is to construct gene interaction networks from microarray data. Time course microarray data is a common place to seek causal relationships between the expression of a regulator and its effect on the expression of its targets. By proposing gene expression patterns of regulator and target genes based on biological expectation of regulatory interactions, it is possible to propose a system to identify these patterns. This system is based on the Correlated Discretized Expression (CDE) score calculated from microarray time course data. The CDE-score is derived by discretizing microarray data to identify significant gene expression changes. The usefulness of this method is demonstrated using a set of hypothetical gene expression data and the analysis of S. cerevisiae cell cycle microarray data.
基因组表达分析的目标之一是从微阵列数据构建基因相互作用网络。时间进程微阵列数据是寻找调节因子表达与其对靶标基因表达影响之间因果关系的常见来源。通过基于调节相互作用的生物学预期提出调节因子和靶标基因的基因表达模式,有可能提出一个识别这些模式的系统。该系统基于从微阵列时间进程数据计算出的相关离散化表达(CDE)分数。CDE分数是通过离散化微阵列数据以识别显著的基因表达变化而得出的。使用一组假设的基因表达数据以及对酿酒酵母细胞周期微阵列数据的分析证明了该方法的有效性。