A review of independent component analysis application to microarray gene expression data.
A review of independent component analysis application to microarray gene expression data.
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DOI：
10.2144/000112950
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发表时间：
2008-11
期刊：
影响因子：
2.7
通讯作者：
Huang X
中科院分区：
文献类型：
--
作者：
Kong W;Vanderburg CR;Gunshin H;Rogers JT;Huang X
独立成分分析（伊卡）方法作为微阵列基因表达数据的有效数据挖掘工具受到越来越多的关注。伊卡作为一种高阶统计分析技术，能够从微阵列数据中提取生物相关的基因表达特征。本文综述了伊卡在基因聚类、分类和识别中的最新应用及其扩展算法。本文描述了伊卡的理论框架，进一步说明了ICA在微阵列数据分析中的特征提取功能。
Independent component analysis (ICA) methods have received growing attention as effective data-mining tools for microarray gene expression data. As a technique of higher-order statistical analysis, ICA is capable of extracting biologically relevant gene expression features from microarray data. Herein we have reviewed the latest applications and the extended algorithms of ICA in gene clustering, classification, and identification. The theoretical frameworks of ICA have been described to further illustrate its feature extraction function in microarray data analysis.