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Identifying genes from up-down properties of microarray expression series

机译:从微阵列表达序列的上下特性识别基因

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Motivation: We consider any collection of microarrays that can be ordered to form a progression; for example, as a function of time, severity of disease or dose of a stimulant. By plotting the expression level of each gene as a function of time, or severity, or dose, we form an expression series, or curve, for each gene. While most of these curves will exhibit random fluctuations, some will contain a pattern, and these are the genes that are most likely associated with the quantity used to order them. Results: We introduce a method of identifying the pattern and hence genes in microarray expression curves without knowing what kind of pattern to look for. Key to our approach is the sequence of ups and downs formed by pairs of consecutive data points in each curve. As a benchmark, we blindly identified genes from yeast cell cycles without selecting for periodic or any other anticipated behaviour.
机译:动机:我们考虑可以定序形成进展的任何微阵列集合;例如,作为时间的函数,疾病的严重程度或兴奋剂的剂量。通过绘制每个基因的表达水平作为时间,严重性或剂量的函数,我们形成了每个基因的表达序列或曲线。尽管这些曲线中的大多数会表现出随机波动,但有些会包含模式,而这些基因最有可能与用于订购它们的数量相关。结果:我们介绍了一种识别模式的方法,从而确定了微阵列表达曲线中的基因,而无需知道寻找哪种模式。我们方法的关键是每条曲线中成对的连续数据点形成的起伏序列。作为基准,我们盲目地从酵母细胞周期中鉴定了基因,而没有选择周期性的或任何其他预期的行为。

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