Abstract
Biomedical 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 some of the current mathematical methods applied to this problem: Principal Component Analysis, Partial Least Squares, and the Statistical Classification Strategy. We demonstrate the application of these methods by three examples of their use in analyzing 1H NMR spectra: screening for colon cancer, characterization of thyroid cancer, and distinguishing cancer from cholangitis in the biliary tract.
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- FLD:
-
Fisher’s linear discriminant
- FOBT:
-
Fecal occult blood test
- NMR:
-
Nuclear magnetic resonance
- PC:
-
Principal component
- PCA:
-
Principal component analysis
- PCR:
-
Principal component regression
- PLS:
-
Partial least squares
- PSC:
-
Primary sclerosing cholangitis
- SCS:
-
Statistical classification strategy
- SIMCA:
-
Soft independent modelling of class analogies
- WCVBST:
-
Weighted cross validated bootstrap
References
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Smith, I.C., Somorjai, R.L. (2011). Deriving Biomedical Diagnostics from Spectroscopic Data. In: Brnjas-Kraljević, J., Pifat-Mrzljak, G. (eds) Supramolecular Structure and Function 10. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-0893-8_7
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DOI: https://doi.org/10.1007/978-94-007-0893-8_7
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