Leach Martin
Bioinformatics, CuraGen Corporation, New Haven, CT, USA.
Methods Mol Biol. 2004;258:153-65. doi: 10.1385/1-59259-751-3:153.
There are many methodologies for performing gene expression profiling on transcripts, and through their use scientists have been generating vast amounts of experimental data. Turning the raw experimental data into meaningful biological observation requires a number of processing steps; to remove noise, to identify the "true" expression value, normalize the data, compare it to reference data, and to extract patterns, or obtain insight into the underlying biology of the samples being measured. In this chapter we give a brief overview of how the raw data is processed, provide details on several data-mining methods, and discuss the future direction of expression informatics.
有许多方法可用于对转录本进行基因表达谱分析，通过这些方法，科学家们已经生成了大量的实验数据。将原始实验数据转化为有意义的生物学观察结果需要一系列处理步骤；去除噪声、识别“真实”表达值、对数据进行归一化、将其与参考数据进行比较，以及提取模式，或者深入了解被测量样本的潜在生物学特性。在本章中，我们简要概述原始数据是如何处理的，详细介绍几种数据挖掘方法，并讨论表达信息学的未来发展方向。
