Deriving biomedical diagnostics from NMR spectroscopic data

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DOIResolve DOI: http://doi.org/10.1007/s12551-011-0045-8
AuthorSearch for: ; Search for:
TypeArticle
Journal titleBiophysical Reviews
Volume3
Issue1
Pages4752; # of pages: 6
SubjectBiomedical spectroscopy; Statistical classification strategy (SCS); Soft independent modelling of class analogies (SIMCA); Cancer screening; 1H NMR spectra
AbstractBiomedical spectroscopic experiments generate large volumes of data. For accurate, robust diagnostic tools the data must be analyzed for only a few characteristic observations per subject, and a large number of subjects must be studied. We describe here two of the current data analytic approaches applied to this problem: SIMCA (principal component analysis, partial least squares), and the statistical classification strategy (SCS). We demonstrate the application of the SCS by three examples of its use in analyzing 1H NMR spectra: screening for colon cancer, characterization of thyroid cancer, and distinguishing cancer from cholangitis in the biliary tract.
Publication date
LanguageEnglish
AffiliationNRC Institute for Biodiagnostics; National Research Council Canada
Peer reviewedYes
NPARC number19703837
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Record identifier6aaae1db-d99e-47c6-8480-da7a81d50a37
Record created2012-03-28
Record modified2016-05-09
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