嵌套效应模型中的结构学习
Structure Learning in Nested Effects Models
摘要：
嵌套效应模型（NEMS）是一类用于分析基因干扰筛选结果的图形模型。NEM探索了表型研究的高维输出之间的噪声子集关系，例如，在基因表达谱中显示的效应或作为扰动细胞的形态学特征。在本文中，我们扩大了统计基础的NEM在四个方向：首先，我们推导出一个新的公式的似然函数的NEM，推广以前的结果为二进制数据。其次，我们证明了模型的可识别性在温和的假设。第三，我们表明，新的制定的可能性允许有效地遍历模型空间。第四，我们结合先验知识和自动变量选择标准，以减少数据中噪声的影响。
英文摘要：
Nested Effects Models (NEMs) are a class of graphical models introduced to analyze the results of gene perturbation screens. NEMs explore noisy subset relations between the high-dimensional outputs of phenotyping studies, e.g. the effects showing in gene expression profiles or as morphological features of the perturbed cell. In this paper we expand the statistical basis of NEMs in four directions: First, we derive a new formula for the likelihood function of a NEM, which generalizes previous results for binary data. Second, we prove model identifiability under mild assumptions. Third, we show that the new formulation of the likelihood allows to efficiently traverse model space. Fourth, we incorporate prior knowledge and an automated variable selection criterion to decrease the influence of noise in the data.
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