Quackenbush J
Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA.
Yearb Med Inform. 2006:91-103.
To review the current state of the art in computational methods for the analysis of DNA microarray data.
The review considers methods of microarray data collection, transformation and representation, comparisons and predictions of gene expression from the data, their mechanistic analysis, related systems biology, and the application of clustering techniques.
Functional genomics approaches have greatly increased the rate at which data on biological systems is generated, leading to corresponding challenges in analyzing the data through advanced computational techniques. The paper compares and contrasts the application of computational clustering for discovery, comparison, and prediction of gene expression classes, together with their evaluation and relation to mechanistic analyses of biological systems.
Methods for assaying gene expression levels by DNA microarray experiments produce considerably more data than other techniques, and require a wide variety of computational techniques for identifying patterns of expression that may be biologically significant. These will have to be verified and validated by comparison to results from other methods, integrated with other systems data, and provide the feedback for further experimentation for testing mechanistic or other biological hypotheses.
回顾用于分析DNA微阵列数据的计算方法的当前技术水平。
本综述考虑了微阵列数据收集、转换和表示的方法，从数据中进行基因表达的比较和预测、其机制分析、相关的系统生物学以及聚类技术的应用。
功能基因组学方法极大地提高了生物系统数据的生成速率，从而在通过先进计算技术分析数据方面带来了相应挑战。本文比较并对比了计算聚类在基因表达类别发现、比较和预测中的应用，以及它们的评估和与生物系统机制分析的关系。
通过DNA微阵列实验测定基因表达水平的方法产生的数据比其他技术多得多，并且需要各种各样的计算技术来识别可能具有生物学意义的表达模式。这些必须通过与其他方法的结果进行比较来验证和确认，与其他系统数据整合，并为进一步实验提供反馈以检验机制或其他生物学假设。
