Gene expression data analysis
Gene expression data analysis
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DOI：
10.1016/s1286-4579(01)01440-x
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发表时间：
2001-08-01
影响因子：
5.8
通讯作者：
Vilo, J
中科院分区：
文献类型：
--
作者：
Brazma, A;Vilo, J
微阵列是实验分子生物学的最新突破之一，它可以并行监测数万个基因的基因表达，并已经产生了大量有价值的数据。对这些数据的分析和处理正在成为利用该技术的主要瓶颈之一。原始微阵列数据是图像，其必须被转换成基因表达矩阵，表格中的行代表基因，列代表各种样品，如组织或实验条件，每个单元中的数字表征特定样品中特定基因的表达水平。如果要提取有关潜在生物过程的任何知识，必须进一步分析这些矩阵。在本文中，我们集中讨论用于这种分析的生物信息学方法。我们简要讨论了监督和非监督数据分析及其应用，如预测基因功能类和癌症分类以及一些可能的未来方向。(C)2001年版科学与医学Elsevier SAS。
Microarrays are one of the latest breakthroughs in experimental molecular biology, which allow monitoring of gene expression for tens of thousands of genes in parallel and are already producing huge amounts of valuable data. Analysis and handling of such data is becoming one of the major bottlenecks in the utilization of the technology. The raw microarray data are images, which have to be transformed into gene expression matrices, tables where rows represent genes, columns represent various samples such as tissues or experimental conditions, and numbers in each cell characterize the expression level of the particular gene in the particular sample. These matrices have to be analyzed further if any knowledge about the underlying biological processes is to be extracted. In this paper we concentrate on discussing bioinformatics methods used for such analysis. We briefly discuss supervised and unsupervised data analysis and its applications, such as predicting gene function classes and cancer classification as well as some possible future directions. (C) 2001 editions scientifiques et medicales Elsevier SAS.