Journal
BIOMETRICS
Volume 62, Issue 2, Pages 589-597Publisher
WILEY
DOI: 10.1111/j.1541-0420.2005.00504.x
Keywords
feature extraction; mass spectrometry; multiscale structure; wavelets
Funding
- NCI NIH HHS [P01CA053996, U01CA086368] Funding Source: Medline
- NIGMS NIH HHS [K25-GM67211] Funding Source: Medline
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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.
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