Pattern recognition methods and applications in biomedical magnetic resonance

Lindon, John C.; Holmes, Elaine; Nicholson, Jeremy K.

doi:10.1016/s0079-6565(00)00036-4

Cited by 396 publications

(257 citation statements)

References 85 publications

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“…To explore the metabolite multidimensional data set, we used two unsupervised analytical methods: PCA (Lindon et al, 2001) and the SOM algorithm (Kohonen, 2001), which is an application of Artificial Neural Networks. PCA was performed, on mean-centered data scaled to unit variance, using SAS software version 8.01 (SAS Institute, 1990).…”

Section: Discussionmentioning

confidence: 99%

Gene and Metabolite Regulatory Network Analysis of Early Developing Fruit Tissues Highlights New Candidate Genes for the Control of Tomato Fruit Composition and Development

et al. 2009

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Variations in early fruit development and composition may have major impacts on the taste and the overall quality of ripe tomato (Solanum lycopersicum) fruit. To get insights into the networks involved in these coordinated processes and to identify key regulatory genes, we explored the transcriptional and metabolic changes in expanding tomato fruit tissues using multivariate analysis and gene-metabolite correlation networks. To this end, we demonstrated and took advantage of the existence of clear structural and compositional differences between expanding mesocarp and locular tissue during fruit development (12-35 d postanthesis). Transcriptome and metabolome analyses were carried out with tomato microarrays and analytical methods including proton nuclear magnetic resonance and liquid chromatography-mass spectrometry, respectively. Pairwise comparisons of metabolite contents and gene expression profiles detected up to 37 direct gene-metabolite correlations involving regulatory genes (e.g. the correlations between glutamine, bZIP, and MYB transcription factors). Correlation network analyses revealed the existence of major hub genes correlated with 10 or more regulatory transcripts and embedded in a large regulatory network. This approach proved to be a valuable strategy for identifying specific subsets of genes implicated in key processes of fruit development and metabolism, which are therefore potential targets for genetic improvement of tomato fruit quality.

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Section: Discussionmentioning

confidence: 99%

Gene and Metabolite Regulatory Network Analysis of Early Developing Fruit Tissues Highlights New Candidate Genes for the Control of Tomato Fruit Composition and Development

et al. 2009

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“…16 The concept of metabonomics has been distilled from a large number of studies involving the application of NMR spectroscopy to the study of the metabolic composition of a wide range of biofluids and tissues 8,12,15,[22][23][24] and the approach as a whole has been reviewed recently. 25,26 1 H NMR spectroscopy has been successfully used to identify a number of novel metabolic markers of organ-specific toxicity in the rat. 12,13,[27][28][29] Currently, the role of metabonomics in toxicology is being evaluated by the COMET (the Consortium on Metabonomic Toxicology) project, 3 which has generated databases and predictive expert systems based on a wide range of model toxins, using 1 H NMR spectroscopy and chemometric analysis of rodent urine and serum.…”

Section: Nmr Based Metabonomicsmentioning

confidence: 99%

NMR-based metabonomic approaches for evaluating physiological influences on biofluid composition

et al. 2004

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Strategies such as genomics, proteomics and metabonomics are being applied with increasing frequency in the pharmaceutical industry. For each of these approaches, toxicological response can be measured by terms of deviation from control or baseline status. However, in order to accurately define drug-induced response, it is necessary to characterize the normal degree of physiological variation in the absence of stimuli. Here, 1 H NMR spectroscopic-based analyses of the metabolic composition of urine in experimental animals under various normal physiological conditions are reviewed. In particular, the effects of inter-animal and diurnal variation, gender, age, diet, species, strain, hormonal status and stress on the biochemical composition of urine are explored. Pattern recognition methods facilitate the comparison of urine NMR spectra over a given time-course, enabling the establishment of changes in profile and highlighting the dynamic metabolic status of an organism. Thus metabonomic approaches based on information-rich spectroscopic data sets can be used to evaluate normal physiological variation and for investigation of drug safety issues.

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“…Of the analytical techniques used, 1 H-NMR spectroscopy has been shown to be the main metaboliteprofiling tool, as it enables many endogenous metabolites to be quantified rapidly and reproducibly without derivatization or separation (1)(2)(3)(4)(5). The NMR-generated metabolic datasets can be effectively interpreted and classified by the application of multivariate statistical analysis including pattern-recognition methods, such as principal components analysis (PCA) and partial least-squares (PLS) discriminate analysis (1,(5)(6)(7)(8)(9)(10).…”

Section: Introductionmentioning

confidence: 99%

Gender‐related and age‐related urinalysis of healthy subjects by NMR‐based metabonomics

Psihogios¹,

Gazi²,

Elisaf³

et al. 2007

NMR in Biomedicine

137

139

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NMR-based metabonomic analysis is a well-established approach to characterizing healthy and diseased states. The aim of this study was to investigate inter-individual variability in the metabolic urinary profile of a healthy Greek population, not subjected to strict dietary limitations, by NMR-based metabonomics. The overall metabonomic urinalysis showed a homogeneous distribution among the population. The metabolic profile was examined in relation to gender and age, and reference intervals of major metabolites were determined. Multivariate data analysis led to the construction of two robust models that were able to predict the class membership of the subjects studied according to their gender and age. The most influential low molecular weight metabolites responsible for the differences in gender groups were citrate, creatinine, trimethylamine N-oxide, glycine, creatine and taurine, and for the differences in age groups they were citrate, creatinine, trimethylamine N-oxide and an unidentified metabolite (d 3.78).

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Pattern recognition methods and applications in biomedical magnetic resonance

Cited by 396 publications

References 85 publications

Gene and Metabolite Regulatory Network Analysis of Early Developing Fruit Tissues Highlights New Candidate Genes for the Control of Tomato Fruit Composition and Development

Gene and Metabolite Regulatory Network Analysis of Early Developing Fruit Tissues Highlights New Candidate Genes for the Control of Tomato Fruit Composition and Development

NMR-based metabonomic approaches for evaluating physiological influences on biofluid composition

Gender‐related and age‐related urinalysis of healthy subjects by NMR‐based metabonomics

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