A Review of Feature Extraction Software for Microarray Gene Expression Data

被引：12

作者：

Tan, Ching Siang ^{[1
]}

Ting, Wai Soon ^{[1
]}

Mohamad, Mohd Saberi ^{[1
]}

Chan, Weng Howe ^{[1
]}

Deris, Safaai ^{[1
]}

Shah, Zuraini Ali ^{[1
]}

机构：

[1] Univ Teknol Malaysia, Fac Comp, Artificial Intelligence & Bioinformat Res Grp, Skudai 81310, Johor, Malaysia

来源：

BIOMED RESEARCH INTERNATIONAL | 2014年 / 2014卷

关键词：

PARTIAL LEAST-SQUARES; MULTIVARIATE-ANALYSIS; COMPONENT ANALYSIS; R-PACKAGE; CLASSIFICATION; SELECTION; PREDICTION; CANCER;

D O I：

10.1155/2014/213656

中图分类号：

Q81 [生物工程学（生物技术）]; Q93 [微生物学];

学科分类号：

071005 ; 0836 ; 090102 ; 100705 ;

摘要：

When gene expression data are too large to be processed, they are transformed into a reduced representation set of genes. Transforming large-scale gene expression data into a set of genes is called feature extraction. If the genes extracted are carefully chosen, this gene set can extract the relevant information from the large-scale gene expression data, allowing further analysis by using this reduced representation instead of the full size data. In this paper, we review numerous software applications that can be used for feature extraction. The software reviewed is mainly for Principal Component Analysis (PCA), Independent Component Analysis (ICA), Partial Least Squares (PLS), and Local Linear Embedding (LLE). A summary and sources of the software are provided in the last section for each feature extraction method.

引用

页数：15

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