Feature extraction and quantification for mass spectrometry in biomedical applications using the mean spectrum
Author(s) -
J. Steven Morris,
Kevin R. Coombes,
John M. Koomen,
Keith Baggerly,
Ryûji Kobayashi
Publication year - 2005
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/bti254
Subject(s) - computer science , mass spectrometry , matlab , software , scripting language , data mining , feature (linguistics) , pattern recognition (psychology) , artificial intelligence , chemistry , chromatography , linguistics , philosophy , programming language , operating system
Mass spectrometry yields complex functional data for which the features of scientific interest are peaks. A common two-step approach to analyzing these data involves first extracting and quantifying the peaks, then analyzing the resulting matrix of peak quantifications. Feature extraction and quantification involves a number of interrelated steps. It is important to perform these steps well, since subsequent analyses condition on these determinations. Also, it is difficult to compare the performance of competing methods for analyzing mass spectrometry data since the true expression levels of the proteins in the population are generally not known.
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