Symbolic discriminant analysis of microarray data in autoimmune disease
Symbolic discriminant analysis of microarray data in autoimmune disease
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DOI：
10.1002/gepi.1117
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发表时间：
2002-06-01
影响因子：
2.1
通讯作者：
Aune, TM
中科院分区：
文献类型：
--
作者：
Moore, JH;Parker, JS;Aune, TM
新的实验室技术，如DNA微阵列，使人们能够同时测量特定细胞或组织中数千个基因的表达水平。遗传流行病学家面临的挑战将是开发统计和计算方法，能够识别分类和预测临床终点的基因表达变量子集。线性判别分析是一种流行的多变量统计方法，用于将观测值分类成组。这是因为该理论被很好地描述，并且该方法易于实现和解释。然而，一个重要的限制是，线性判别函数需要预先指定。为了解决这个限制和线性的限制，我们已经开发了符号判别分析（SDA）的基因表达变量和判别函数，可以采取任何形式的自动选择。在本研究中，我们证明SDA能够识别能够分类和预测自身免疫性疾病的基因表达变量的组合。(C)2002 Wiley-Liss，Inc.
New laboratory technologies such as DNA microarrays have made it possible to measure the expression levels of thousands of genes simultaneously in a particular cell or tissue. The challenge for genetic epidemiologists will be to develop statistical and computational methods that are able to identify subsets of gene expression variables that classify and predict clinical endpoints. Linear discriminant analysis is a popular multivariate statistical approach for classification of observations into groups. This is because the theory is well described and the method is easy to implement and interpret. However, an important limitation is that linear discriminant functions need to be prespecified. To address this limitation and the limitation of linearity, we have developed symbolic discriminant analysis (SDA) for the automatic selection of gene expression variables and discriminant functions that can take any form. In the present study, we demonstrate that SDA is capable of identifying combinations of gene expression variables that are able to classify and predict autoimmune diseases. (C) 2002 Wiley-Liss, Inc.