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Efficient Peak-Labeling Algorithms for Whole-Sample Mass Spectrometry Proteomics
January-March 2010 (vol. 7 no. 1)
pp. 126-137
ASCII Text
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Richard Pelikan, Milos Hauskrecht, "Efficient Peak-Labeling Algorithms for Whole-Sample Mass Spectrometry Proteomics," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 7, no. 1, pp. 126-137, January-March, 2010.
 
 
BibTex
x
 
@article{ 10.1109/TCBB.2008.31,
author = {Richard Pelikan and Milos Hauskrecht},
title = {Efficient Peak-Labeling Algorithms for Whole-Sample Mass Spectrometry Proteomics},
journal ={IEEE/ACM Transactions on Computational Biology and Bioinformatics},
volume = {7},
number = {1},
issn = {1545-5963},
year = {2010},
pages = {126-137},
doi = {http://doi.ieeecomputersociety.org/10.1109/TCBB.2008.31},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE/ACM Transactions on Computational Biology and Bioinformatics
TI - Efficient Peak-Labeling Algorithms for Whole-Sample Mass Spectrometry Proteomics
IS - 1
SN - 1545-5963
SP126
EP137
EPD - 126-137
A1 - Richard Pelikan,
A1 - Milos Hauskrecht,
PY - 2010
KW - Machine learning
KW - biology and genetics
KW - heuristics design.
VL - 7
JA - IEEE/ACM Transactions on Computational Biology and Bioinformatics
ER -
 
Richard Pelikan, University of Pittsburgh, Pittsburgh
Milos Hauskrecht, University of Pittsburgh, Pittsburgh
Whole-sample mass spectrometry (MS) proteomics allows for a parallel measurement of hundreds of proteins present in a variety of biospecimens. Unfortunately, the association between MS signals and these proteins is not straightforward. The need to interpret mass spectra demands the development of methods for accurate labeling of ion species in such profiles. To aid this process, we have developed a new peak-labeling procedure for associating protein and peptide labels with peaks. This computational method builds upon characteristics of proteins expected to be in the sample, such as the amino sequence, mass weight, and expected concentration within the sample. A new probabilistic score that incorporates this information is proposed. We evaluate and demonstrate our method's ability to label peaks first on simulated MS spectra and then on MS spectra from human serum with a spiked-in calibration mixture.

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Index Terms:
Machine learning, biology and genetics, heuristics design.
Citation:
Richard Pelikan, Milos Hauskrecht, "Efficient Peak-Labeling Algorithms for Whole-Sample Mass Spectrometry Proteomics," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 7, no. 1, pp. 126-137, Jan.-March 2010, doi:10.1109/TCBB.2008.31
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