Classification of toxin-induced changes in 1H NMR spectra of urine using an artificial neural network

Anthony, Maria L.; Rose, Valerie S.; Nicholson, Jeremy K.; Lindon, John C.

doi:10.1016/0731-7085(95)01278-s

Cited by 63 publications

(17 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…PCA, an unsupervised projection method used to visualize the dataset and display the similarity and difference, was performed [10,14,24,25]. The PCA scores plot showed the two groups were scattered into two different regions ( Fig.…”

Section: Pattern Recognition and Function Analysismentioning

confidence: 99%

Metabolomic study for diagnostic model of oesophageal cancer using gas chromatography/mass spectrometry

Xue

et al. 2009

Journal of Chromatography B

View full text Add to dashboard Cite

Section: Pattern Recognition and Function Analysismentioning

confidence: 99%

Metabolomic study for diagnostic model of oesophageal cancer using gas chromatography/mass spectrometry

Xue

et al. 2009

Journal of Chromatography B

View full text Add to dashboard Cite

“…Anthony et al applied neural network methods to classify toxin-induced changes in the levels of 18 low-molecular-weight metabolites in rat urine as determined by proton NMR spectra (47). From a limited data set, the network predicted the class of the toxin and hence the target organ.…”

Section: Classification and Clusteringmentioning

confidence: 99%

Artificial neural networks for pattern recognition

Corne¹

1996

Concepts Magn. Reson.

View full text Add to dashboard Cite

“…The detected peaks were normalized to the total sum of these peak areas in Matlab software 7.0 (The MathWorks) in order to eliminate the disparity of urine volume. PCA and projections to latent structures-discriminant analysis (PLS-DA) [34,35] were carried out in SIMCA-P 11.0 (Umetrics, Umeå, Sweden) using mean-centered, auto-scaled (scaled to unit variance) data. The PCA scores plot represents the distribution of samples in multivariate space where each coordinate denotes each subject.…”

Section: Data Processing and Pattern Recognitionmentioning

confidence: 99%

Analysis of urinary metabolites for metabolomic study by pressurized CEC

Xie

Peng³

et al. 2007

Electrophoresis

View full text Add to dashboard Cite

A new approach for the metabolomic study of urinary samples using pressurized CEC (pCEC) with gradient elution is proposed as an alternative chromatographic separation tool with higher degree of resolution, selectivity, sensitivity, and efficiency. The pCEC separation of urinary samples was performed on a RP column packed with C(18), 5 microm particles with an ACN/water mobile phase containing TFA. The effects of the acid modifiers, applied voltage, mobile phase, and detection wavelength were systematically evaluated using eight spiked standards, as well as urine samples. A typical analytical trial of urine samples from Sprague Dawley (S.D.) rats exposed to high-energy diet was carried out following sample pretreatment. Significant differences in urinary metabolic profiles were observed between the high energy diet-induced obesity rats and the healthy control rats at the 6th wk postdose. Multivariate statistical analysis revealed the differential metabolites in response to the diet, which were partially validated with the putative standards. This work suggests that such a pCEC-based separation and analysis method may provide a new and cost-effective platform for metabolomic study uniquely positioned between the conventional chromatographic tools such as HPLC, and hyphenated analytical techniques such as LC-MS.

show abstract

Classification of toxin-induced changes in 1H NMR spectra of urine using an artificial neural network

Cited by 63 publications

References 11 publications

Metabolomic study for diagnostic model of oesophageal cancer using gas chromatography/mass spectrometry

Metabolomic study for diagnostic model of oesophageal cancer using gas chromatography/mass spectrometry

Artificial neural networks for pattern recognition

Analysis of urinary metabolites for metabolomic study by pressurized CEC

Contact Info

Product

Resources

About