Imperial College London

Emeritus ProfessorJeremyNicholson

Faculty of MedicineDepartment of Metabolism, Digestion and Reproduction

Emeritus Professor of Biological Chemistry
 
 
 
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Contact

 

+44 (0)20 7594 3195j.nicholson Website

 
 
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Assistant

 

Ms Wendy Torto +44 (0)20 7594 3225

 
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Location

 

Office no. 665Sir Alexander Fleming BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Gartland:1990:10.1002/nbm.1940030404,
author = {Gartland, KP and Sanins, SM and Nicholson, JK and Sweatman, BC and Beddell, CR and Lindon, JC},
doi = {10.1002/nbm.1940030404},
journal = {NMR Biomed},
pages = {166--172},
title = {Pattern recognition analysis of high resolution 1H NMR spectra of urine. A nonlinear mapping approach to the classification of toxicological data.},
url = {http://dx.doi.org/10.1002/nbm.1940030404},
volume = {3},
year = {1990}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - A computer-based pattern recognition (PR) approach has been applied to the interpretation of 1H NMR generated urinalysis data in a variety of experimental toxicity states in the rat. 1H NMR signal intensities for each endogenous metabolite in urine were regarded as coordinates in multi-dimensional space and analysed using computer pattern recognition methods through which the dimensionality was reduced for display and categorization purposes. Initially 17 metabolic dimensions were used which were defined by the scored relative concentrations of a variety of urinary metabolites detected in 1H NMR spectra. By employing the unsupervised learning methods of 2- and 3-dimensional nonlinear mapping (NLM) different types of toxin (hepatotoxins, cortical and papillary nephrotoxins) could be classified according to NMR-detectable biochemical effects in the urine. The robustness of the classification methods, and the influence of the addition of new scored biochemical data reflecting dose response situations, nutritional effects on toxicity, sex differences in biochemical response to toxins and addition of a new toxin class (testicular toxin) to the pattern recognition analysis were also evaluated. We find that the initial training set maps are fundamentally stable to the addition of all data types and that the PR methods correctly 'predicted' the toxicological effects of the test compounds. These results confirm the power and wide applicability of linked PR and 1H NMR urinalysis as an approach to the generation and classification of acute toxicological data.
AU - Gartland,KP
AU - Sanins,SM
AU - Nicholson,JK
AU - Sweatman,BC
AU - Beddell,CR
AU - Lindon,JC
DO - 10.1002/nbm.1940030404
EP - 172
PY - 1990///
SN - 0952-3480
SP - 166
TI - Pattern recognition analysis of high resolution 1H NMR spectra of urine. A nonlinear mapping approach to the classification of toxicological data.
T2 - NMR Biomed
UR - http://dx.doi.org/10.1002/nbm.1940030404
UR - https://www.ncbi.nlm.nih.gov/pubmed/2206848
VL - 3
ER -