首页> 外文期刊>Journal of biomedical informatics. >Cross-generation and cross-laboratory predictions of Affymetrix microarrays by rank-based methods.
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Cross-generation and cross-laboratory predictions of Affymetrix microarrays by rank-based methods.

机译:通过基于等级的方法对Affymetrix微阵列进行跨代和跨实验室的预测。

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Past experiments of the popular Affymetrix (Affy) microarrays have accumulated a huge amount of public data sets. To apply them for more wide studies, the comparability across generations and experimental environments is an important research topic. This paper particularly investigates the issue of cross-generation/laboratory predictions. That is, whether models built upon data of one generation (laboratory) can differentiate data of another. We consider eight public sets of three cancers. They are from different laboratories and are across various generations of Affy human microarrays. Each cancer has certain subtypes, and we investigate if a model trained from one set correctly differentiates another. We propose a simple rank-based approach to make data from different sources more comparable. Results show that it leads to higher prediction accuracy than using expression values. We further investigate normalization issues in preparing training/testing data. In addition, we discuss some pitfalls in evaluating cross-generation/laboratory predictions. To use data from various sources one must be cautious on some important but easily neglected steps.
机译:过去流行的Affymetrix(Affy)微阵列实验已经积累了大量公共数据集。为了将它们应用到更广泛的研究中,跨代和实验环境之间的可比性是一个重要的研究主题。本文特别研究跨代/实验室预测的问题。也就是说,建立在一代(实验室)数据上的模型是否可以区分另一一代的数据。我们考虑三种癌症的八种公共场所。它们来自不同的实验室,跨越了Affy人类微阵列的各个世代。每种癌症都有某些亚型,我们将研究从一组训练出的模型是否可以正确区分另一种。我们提出一种简单的基于等级的方法,以使来自不同来源的数据更具可比性。结果表明,与使用表达式值相比,它可以提高预测精度。我们会在准备培训/测试数据时进一步调查归一化问题。此外,我们讨论了评估跨代/实验室预测时的一些陷阱。要使用来自各种来源的数据,必须对一些重要但容易被忽略的步骤保持谨慎。

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