Clustering of high throughput gene expression data

被引：78

作者：

Pirim, Harun ^{[1
]}

Eksioglu, Burak ^{[1
]}

Perkins, Andy D. ^{[2
]}

Yuceer, Cetin ^{[3
]}

机构：

[1] Mississippi State Univ, Dept Ind & Syst Engn, Mississippi State, MS 39762 USA

[2] Mississippi State Univ, Dept Comp Sci & Engn, Mississippi State, MS 39762 USA

[3] Mississippi State Univ, Dept Forestry, Mississippi State, MS 39762 USA

来源：

COMPUTERS & OPERATIONS RESEARCH | 2012年 / 39卷 / 12期

基金：

美国国家科学基金会; 美国国家卫生研究院;

关键词：

Clustering; Bioinformatics; Gene expression data; High throughput data; Microarrays; COMMUNITY STRUCTURE; VARIABLE SELECTION; MICROARRAY DATA; ALGORITHM; OPTIMIZATION; MODEL; NETWORKS; CLASSIFICATION; PREDICTION; FRAMEWORK;

D O I：

10.1016/j.cor.2012.03.008

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

High throughput biological data need to be processed, analyzed, and interpreted to address problems in life sciences. Bioinformatics, computational biology, and systems biology deal with biological problems using computational methods. Clustering is one of the methods used to gain insight into biological processes, particularly at the genomics level. Clearly, clustering can be used in many areas of biological data analysis. However, this paper presents a review of the current clustering algorithms designed especially for analyzing gene expression data. It is also intended to introduce one of the main problems in bioinformatics - clustering gene expression data - to the operations research community. (c) 2012 Elsevier Ltd. All rights reserved.

引用

页码：3046 / 3061

页数：16

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