Blind source separation methods for deconvolution cancer biology

被引：23

作者：

Zinovyev, Andrei ^{[1
,2
,3
]}

Kairov, Ulykbek ^{[4
,5
]}

Karpenyuk, Tatyana ^{[4
]}

Ramanculov, Erlan ^{[5
]}

机构：

[1] Inst Curie, Paris, France

[2] INSERM, U900, Paris, France

[3] Mines ParisTech, Fontainebleau, France

[4] Kazakh Natl Univ, Alma Ata, Kazakhstan

[5] Natl Ctr Biotechnol Republ Kazakhstan, Astana, Kazakhstan

来源：

BIOCHEMICAL AND BIOPHYSICAL RESEARCH COMMUNICATIONS | 2013年 / 430卷 / 03期

关键词：

Cancer; Gene expression; Data analysis; Linear data approximation; Independent component analysis; Non-negative matrix factorization; INDEPENDENT COMPONENT ANALYSIS; MATRIX FACTORIZATION METHODS; MICROARRAY DATA; TUMOR CLASSIFICATION; DISCOVERY; MODEL;

D O I：

10.1016/j.bbrc.2012.12.043

中图分类号：

Q5 [生物化学]; Q7 [分子生物学];

学科分类号：

071010 ; 081704 ;

摘要：

Two blind source separation methods (Independent Component Analysis and Non-negative Matrix Factorization), developed initially for signal processing in engineering, found recently a number of applications in analysis of large-scale data in molecular biology. In this short review, we present the common idea behind these methods, describe ways of implementing and applying them and point out to the advantages compared to more traditional statistical approaches. We focus more specifically on the analysis of gene expression in cancer. The review is finalized by listing available software implementations for the methods described. (C) 2012 Elsevier Inc. All rights reserved.

引用

页码：1182 / 1187

页数：6

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