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应对异质性的分类方法。

Classification methods for confronting heterogeneity.

作者信息

Province M A, Shannon W D, Rao D C

机构信息

Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri 63110, USA.

出版信息

Adv Genet. 2001;42:273-86. doi: 10.1016/s0065-2660(01)42028-1.

Abstract

Recursive partitioning/tree models are discussed as a method of dissecting the complex nature of traits with different causal mechanisms operating in different subsets of the data (e.g., different genes operating in different subsets of families). In addition to the straightforward application of classification and regression trees to define more homogeneous subsets of the data on which to conduct further analysis, developments incorporating linkage analysis into the definition of the regression trees (Shannon et al., 2000) are discussed. The pros and cons of recursive partitioning vs. the related approach of context-dependent analysis (Turner et al., 1999) are also reviewed as two promising analysis strategies that may be useful for genetic dissection of complex traits.

摘要

递归划分/树模型被作为一种剖析性状复杂本质的方法进行讨论,其中不同的因果机制在数据的不同子集中起作用(例如,不同的基因在不同的家系子集中起作用)。除了直接应用分类树和回归树来定义更同质的数据子集以进行进一步分析外,还讨论了将连锁分析纳入回归树定义的进展(Shannon等人,2000年)。递归划分与相关的上下文依赖分析方法(Turner等人,1999年)的优缺点也作为两种有前景的分析策略进行了综述,这两种策略可能对复杂性状的基因剖析有用。

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