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

Andrei Zinovyev, Ulykbek Kairov, Tatyana Karpenyuk, Erlan Ramanculov

Research output: Contribution to journalArticle

12 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1182-1187
Number of pages6
JournalBiochemical and Biophysical Research Communications
Volume430
Issue number3
DOIs
Publication statusPublished - Jan 18 2013
Externally publishedYes

Fingerprint

Blind source separation
Deconvolution
Molecular biology
Independent component analysis
Factorization
Gene expression
Neoplasms
Signal processing
Molecular Biology
Software
Gene Expression

Keywords

  • Cancer
  • Data analysis
  • Gene expression
  • Independent component analysis
  • Linear data approximation
  • Non-negative matrix factorization

ASJC Scopus subject areas

  • Biochemistry
  • Biophysics
  • Cell Biology
  • Molecular Biology

Cite this

Blind source separation methods for deconvolution of complex signals in cancer biology. / Zinovyev, Andrei; Kairov, Ulykbek; Karpenyuk, Tatyana; Ramanculov, Erlan.

In: Biochemical and Biophysical Research Communications, Vol. 430, No. 3, 18.01.2013, p. 1182-1187.

Research output: Contribution to journalArticle

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