Sample classification based on Bayesian spectral decomposition of metabonomic NMR data sets

Radka Stoyanova, Jeremy K. Nicholson, John C. Lindon, Truman R. Brown

Research output: Contribution to journalArticle

31 Scopus citations

Abstract

1H NMR spectra of biofluids provides a wealth of biochemical information on the metabolic status of an organism. Through the application of pattern recognition and classification algorithms, the data have been shown to provide information on disease diagnosis and the beneficial and adverse effects of potential therapeutics. Here, a novel approach is described for identifying subsets of spectral patterns in databases of NMR spectra, and it is shown that the intensities of these spectral patterns can be related to the onset and recovery from a toxic lesion in both a time-related and dose-related fashion. These patterns form a new type of combination biomarker for the biological effect under study. The approach is illustrated with a study of liver toxicity in rats using NMR spectra of urine following administration of a model hepatotoxin hydrazine.

Original languageEnglish (US)
Pages (from-to)3666-3674
Number of pages9
JournalAnalytical Chemistry
Volume76
Issue number13
DOIs
StatePublished - Jul 1 2004

ASJC Scopus subject areas

  • Analytical Chemistry

Fingerprint Dive into the research topics of 'Sample classification based on Bayesian spectral decomposition of metabonomic NMR data sets'. Together they form a unique fingerprint.

Cite this