| BUDINSKÁ, Eva, Eva GELNAROVÁ and Michael G. SCHIMEK. MSMAD: a computationally efficient method for the analysis of noisy array CGH data. Bioinformatics. Oxford University Press, 2009, vol. 25, No 6, p. 703-713. ISSN 1367-4803. |
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@article{825041,
author = {Budinská, Eva and Gelnarová, Eva and Schimek, Michael G.},
article_number = {6},
keywords = {MSMAD; microarray; arrayCGH; median absolute deviation; median smoothing},
language = {eng},
issn = {1367-4803},
journal = {Bioinformatics},
title = {MSMAD: a computationally efficient method for the analysis of noisy array CGH data},
url = {http://bioinformatics.oxfordjournals.org/cgi/content/full/25/6/703},
volume = {25},
year = {2009}
}
TY - JOUR
ID - 825041
AU - Budinská, Eva - Gelnarová, Eva - Schimek, Michael G.
PY - 2009
TI - MSMAD: a computationally efficient method for the analysis of noisy array CGH data
JF - Bioinformatics
VL - 25
IS - 6
SP - 703-713
EP - 703-713
PB - Oxford University Press
SN - 13674803
KW - MSMAD
KW - microarray
KW - arrayCGH
KW - median absolute deviation
KW - median smoothing
UR - http://bioinformatics.oxfordjournals.org/cgi/content/full/25/6/703
N2 - Genome analysis has become one of the most important tools for understanding the complex process of cancerogenesis. With increasing resolution of CGH arrays, the demand for computationally efficient algorithms arises, which are effective in the detection of aberrations even in very noisy data. We developed a rather simple, non-parametric technique of high computational efficiency for CGH array analysis that adopts a median absolute deviation concept for breakpoint detection, comprising median smoothing for pre-processing. The resulting algorithm has the potential to outperform any single smoothing approach as well as several recently proposed segmentation techniques. We show its performance through the application of simulated and real datasets in comparison to three other methods for array CGH analysis.
ER -
BUDINSKÁ, Eva, Eva GELNAROVÁ and Michael G. SCHIMEK. MSMAD: a computationally efficient method for the analysis of noisy array CGH data. \textit{Bioinformatics}. Oxford University Press, 2009, vol.~25, No~6, p.~703-713. ISSN~1367-4803.
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