独立成分分析在微阵列基因表达数据中的应用综述。
A review of independent component analysis application to microarray gene expression data.
作者信息
Kong Wei, Vanderburg Charles R, Gunshin Hiromi, Rogers Jack T, Huang Xudong
机构信息
Information Engineering College, Shanghai Maritime University, Shanghai, China.
出版信息
Biotechniques. 2008 Nov;45(5):501-20. doi: 10.2144/000112950.
Abstract
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.
摘要
独立成分分析（ICA）方法作为用于微阵列基因表达数据的有效数据挖掘工具受到了越来越多的关注。作为一种高阶统计分析技术，ICA能够从微阵列数据中提取与生物学相关的基因表达特征。在此，我们综述了ICA在基因聚类、分类和识别方面的最新应用及扩展算法。还描述了ICA的理论框架，以进一步说明其在微阵列数据分析中的特征提取功能。
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