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

Andrei Zinovyev, Ulykbek Kairov, Tatyana Karpenyuk, Erlan Ramanculov

Research output: Contribution to journalShort surveypeer-review

16 Citations (Scopus)


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
Issue number3
Publication statusPublished - Jan 18 2013
Externally publishedYes


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

ASJC Scopus subject areas

  • Biophysics
  • Biochemistry
  • Molecular Biology
  • Cell Biology


Dive into the research topics of 'Blind source separation methods for deconvolution of complex signals in cancer biology'. Together they form a unique fingerprint.

Cite this