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Peakbin selection in mass spectrometry data using a consensus approach with estimation of distribution algorithms

Rubén Armananzas, Yvan Saeys UGent, Iñaki Inza, Miguel Garcia-Torres, Concha Bielza, Yves Van de Peer UGent and Pedro Larranaga (2011) IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS. 8(3). p.760-774
abstract
Progress is continuously being made in the quest for stable biomarkers linked to complex diseases. Mass spectrometers are one of the devices for tackling this problem. The data profiles they produce are noisy and unstable. In these profiles, biomarkers are detected as signal regions (peaks), where control and disease samples behave differently. Mass spectrometry (MS) data generally contain a limited number of samples described by a high number of features. In this work, we present a novel class of evolutionary algorithms, estimation of distribution algorithms (EDA), as an efficient peak selector in this MS domain. There is a trade-of f between the reliability of the detected biomarkers and the low number of samples for analysis. For this reason, we introduce a consensus approach, built upon the classical EDA scheme, that improves stability and robustness of the final set of relevant peaks. An entire data workflow is designed to yield unbiased results. Four publicly available MS data sets (two MALDI-TOF and another two SELDI-TOF) are analyzed. The results are compared to the original works, and a new plot (peak frequential plot) for graphically inspecting the relevant peaks is introduced. A complete online supplementary page, which can be found at http://www.sc.ehu.es/ccwbayes/members/ruben/ms, includes extended info and results, in addition to Matlab scripts and references.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
OVARIAN-CANCER, CLASSIFICATION, SPECTRA, SERUM, BIOINFORMATICS, LASER-DESORPTION, CROSS-VALIDATION, biomarker discovery, feature selection, EDA, Mass spectrometry, REPRODUCIBILITY, OPTIMIZATION, STATISTICS
journal title
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
IEEE-ACM Trans. Comput. Biol. Bioinform.
volume
8
issue
3
pages
760 - 774
Web of Science type
Article
Web of Science id
000288225000015
JCR category
STATISTICS & PROBABILITY
JCR impact factor
1.543 (2011)
JCR rank
24/116 (2011)
JCR quartile
1 (2011)
ISSN
1545-5963
DOI
10.1109/TCBB.2010.18
project
Bioinformatics: from nucleotids to networks (N2N)
language
English
UGent publication?
yes
classification
A1
copyright statement
I have transferred the copyright for this publication to the publisher
id
1209221
handle
http://hdl.handle.net/1854/LU-1209221
date created
2011-04-14 18:19:53
date last changed
2013-02-27 09:11:25
@article{1209221,
  abstract     = {Progress is continuously being made in the quest for stable biomarkers linked to complex diseases. Mass spectrometers are one of the devices for tackling this problem. The data profiles they produce are noisy and unstable. In these profiles, biomarkers are detected as signal regions (peaks), where control and disease samples behave differently. Mass spectrometry (MS) data generally contain a limited number of samples described by a high number of features. In this work, we present a novel class of evolutionary algorithms, estimation of distribution algorithms (EDA), as an efficient peak selector in this MS domain. There is a trade-of f between the reliability of the detected biomarkers and the low number of samples for analysis. For this reason, we introduce a consensus approach, built upon the classical EDA scheme, that improves stability and robustness of the final set of relevant peaks. An entire data workflow is designed to yield unbiased results. Four publicly available MS data sets (two MALDI-TOF and another two SELDI-TOF) are analyzed. The results are compared to the original works, and a new plot (peak frequential plot) for graphically inspecting the relevant peaks is introduced. A complete online supplementary page, which can be found at http://www.sc.ehu.es/ccwbayes/members/ruben/ms, includes extended info and results, in addition to Matlab scripts and references.},
  author       = {Armananzas, Rub{\'e}n and Saeys, Yvan and Inza, I{\~n}aki and Garcia-Torres, Miguel and Bielza, Concha and Van de Peer, Yves and Larranaga, Pedro},
  issn         = {1545-5963},
  journal      = {IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS},
  keyword      = {OVARIAN-CANCER,CLASSIFICATION,SPECTRA,SERUM,BIOINFORMATICS,LASER-DESORPTION,CROSS-VALIDATION,biomarker discovery,feature selection,EDA,Mass spectrometry,REPRODUCIBILITY,OPTIMIZATION,STATISTICS},
  language     = {eng},
  number       = {3},
  pages        = {760--774},
  title        = {Peakbin selection in mass spectrometry data using a consensus approach with estimation of distribution algorithms},
  url          = {http://dx.doi.org/10.1109/TCBB.2010.18},
  volume       = {8},
  year         = {2011},
}

Chicago
Armananzas, Rubén, Yvan Saeys, Iñaki Inza, Miguel Garcia-Torres, Concha Bielza, Yves Van de Peer, and Pedro Larranaga. 2011. “Peakbin Selection in Mass Spectrometry Data Using a Consensus Approach with Estimation of Distribution Algorithms.” Ieee-acm Transactions on Computational Biology and Bioinformatics 8 (3): 760–774.
APA
Armananzas, R., Saeys, Y., Inza, I., Garcia-Torres, M., Bielza, C., Van de Peer, Y., & Larranaga, P. (2011). Peakbin selection in mass spectrometry data using a consensus approach with estimation of distribution algorithms. IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, 8(3), 760–774.
Vancouver
1.
Armananzas R, Saeys Y, Inza I, Garcia-Torres M, Bielza C, Van de Peer Y, et al. Peakbin selection in mass spectrometry data using a consensus approach with estimation of distribution algorithms. IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS. 2011;8(3):760–74.
MLA
Armananzas, Rubén, Yvan Saeys, Iñaki Inza, et al. “Peakbin Selection in Mass Spectrometry Data Using a Consensus Approach with Estimation of Distribution Algorithms.” IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 8.3 (2011): 760–774. Print.