生物通路作为微阵列数据分类的特征

Brian Quanz, Meeyoung Park, Jun Huan
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引用次数: 10

摘要

利用微阵列基因表达数据进行分类是生物信息学的一项重要任务。由于微阵列数据具有高维数和小样本量的特点,最近出现了一种将除表达数据外的任何可用信息纳入分类过程的趋势。因此,利用基因表达数据选择与感兴趣的临床结果密切相关的生物学途径已经开始了许多工作,并且结合这些途径信息为分类开辟了新的途径。与以往将个体基因视为特征的方法相反,我们提出了一种将生物途径视为特征的新方法。每个与感兴趣的结果显著相关的路径被视为一个特征,并被映射到一个特征值。我们定义了几种映射路径到特征的方法,并比较了使用我们的特征转换的几种分类器与使用单个基因作为不同特征选择方法的分类器的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Biological pathways as features for microarray data classification
Classification using microarray gene expression data is an important task in bioinformatics. Due to the high dimensionality and small sample size that characterizes microarray data, there has recently been a drive to incorporate any available information in addition to the expression data in the classification process. As a result, much work has begun on selecting biological pathways that are closely related to a clinical outcome of interest using the gene expression data, and incorporating this pathway information opens up new avenues for classification. As opposed to previous approaches that consider individual genes as features, we propose a new approach that treats biological pathways as features. Each pathway found to be significantly related to an outcome of interest is treated as a feature, and is mapped to a feature value. We define several methods for mapping pathways to features, and compare the performance of several classifiers using our feature transformations to that of the classifiers using individual genes as features for different feature selection methods.
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