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Multiscale Processing of Mass Spectrometry Data
Author(s) -
Randolph T. W.,
Yasui Y.
Publication year - 2006
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2005.00504.x
Subject(s) - pattern recognition (psychology) , histogram , computer science , wavelet , focus (optics) , scale (ratio) , feature (linguistics) , artificial intelligence , image (mathematics) , physics , linguistics , philosophy , quantum mechanics , optics
Summary This work addresses the problem of extracting signal content from protein mass spectrometry data. A multiscale decomposition of these spectra is used to focus on local scale‐based structure by defining scale‐specific features. Quantification of features is accompanied by an efficient method for calculating the location of features which avoids estimation of signal‐to‐noise ratios or bandwidths. Scale‐based histograms serve as spectral‐density‐like functions indicating the regions of high density of features in the data. These regions provide bins within which features are quantified and compared across samples. As a preliminary step, the locations of prominent features within coarse‐scale bins may be used for a crude registration of spectra. The multiscale decomposition, the scale‐based feature definition, the calculation of feature locations, and subsequent quantification of features are carried out by way of a translation‐invariant wavelet analysis.