Microarray data analysis and mining approaches
Microarray data analysis and mining approaches
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DOI：
10.1093/bfgp/elm034
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发表时间：
2007-12-01
期刊：
影响因子：
--
通讯作者：
Calogero, Raffaele A.
中科院分区：
文献类型：
--
作者：
Cordero, Francesca;Botta, Marco;Calogero, Raffaele A.
基于微阵列的转录谱分析现在是一种整合的方法，并且在诸如药物基因组学、诊断学和药物靶点鉴定等领域中具有广泛的用途。大规模的微阵列研究也正成为一种新的实验生物学方法的关键。微阵列转录谱分析的主要问题是数据分析和挖掘。当微阵列成为一种普遍使用的方法时，人们做出了相当大的努力来产生用于鉴定差异表达基因的算法和方法。最近，焦点已经转向微阵列数据挖掘的算法和数据库开发。此外，微阵列技术的发展使研究人员能够掌握转录的调节性质，将基本表达分析与mRNA特征（即基于外显子的阵列）和DNA特征（即比较基因组杂交、单核苷酸多态性、平铺和启动子结构）相结合。在这篇文章中，我们将审查用于检测差异表达的基因和特定的生物学功能的差异表达的链接的方法。
Microarray based transcription profiling is now a consolidated methodology and has widespread use in areas such as pharmacogenomics, diagnostics and drug target identification. Large-scale microarray studies are also becoming crucial to a new way of conceiving experimental biology A main issue in microarray transcription profiling is data analysis and mining. When microarrays became a methodology of general use, considerable effort was made to produce algorithms and methods for the identification of differentially expressed genes. More recently, the focus has switched to algorithms and database development for microarray data mining. Furthermore, the evolution of microarray technology is allowing researchers to grasp the regulative nature of transcription, integrating basic expression analysis with mRNA characteristics, i.e. exon-based arrays, and with DNA characteristics, i.e. comparative genomic hybridization, single nucleotide polymorphism, tiling and promoter structure. In this article, we will review approaches used to detect differentially expressed genes and to link differential expression to specific biological functions.