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 language | English |
|---|---|
| Pages (from-to) | 1182-1187 |
| Number of pages | 6 |
| Journal | Biochemical and Biophysical Research Communications |
| Volume | 430 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - Jan 18 2013 |
| Externally published | Yes |
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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 journal › Article
}
TY - JOUR
T1 - Blind source separation methods for deconvolution of complex signals in cancer biology
AU - Zinovyev, Andrei
AU - Kairov, Ulykbek
AU - Karpenyuk, Tatyana
AU - Ramanculov, Erlan
PY - 2013/1/18
Y1 - 2013/1/18
N2 - 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.
AB - 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.
KW - Cancer
KW - Data analysis
KW - Gene expression
KW - Independent component analysis
KW - Linear data approximation
KW - Non-negative matrix factorization
UR - http://www.scopus.com/inward/record.url?scp=84872502291&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84872502291&partnerID=8YFLogxK
U2 - 10.1016/j.bbrc.2012.12.043
DO - 10.1016/j.bbrc.2012.12.043
M3 - Article
C2 - 23261450
AN - SCOPUS:84872502291
VL - 430
SP - 1182
EP - 1187
JO - Biochemical and Biophysical Research Communications
JF - Biochemical and Biophysical Research Communications
SN - 0006-291X
IS - 3
ER -