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Blind source separation methods for deconvolution of complex signals in cancer biology.

Abstract：

: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.

journal_name

Biochem Biophys Res Commun

journal_title

Biochemical and biophysical research communications

authors

Zinovyev A,Kairov U,Karpenyuk T,Ramanculov E

doi

10.1016/j.bbrc.2012.12.043

subject

Has Abstract

pub_date

2013-01-18 00:00:00

pages

1182-7

issue

3

eissn

0006-291X

issn

1090-2104

pii

S0006-291X(12)02374-1

journal_volume

430

pub_type

杂志文章,评审

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