Pattern Recognition Used to Investigate Multivariate Data in Analytical Chemistry

Jurs, Peter C.

doi:10.1126/science.3704647

Cited by 85 publications

(33 citation statements)

References 53 publications

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“…3). Data capture is most often based on NMR spectroscopy and MS. First metabolomics studies were carried out by NMR spectroscopy [61], a technique characterized by its high reproducibility but also by its limited sensitivity. More sensitive MS techniques hyphenated to gas or liquid have more recently been applied to metabolomics studies [24,62].…”

Section: Metabolomics and Biomarker Discoverymentioning

confidence: 99%

The complex links between dietary phytochemicals and human health deciphered by metabolomics

Manach

Hubert

Llorach

et al. 2009

Molecular Nutrition Food Res

195

144

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A large variety of phytochemicals commonly consumed with the human diet, influence health and may contribute to the prevention of diseases. However, it is still difficult to make nutritional recommendations for these bioactive compounds. Current studies of phytochemicals are generally focused on specific compounds and their effects on a limited number of markers. New approaches are needed to take into account both the diversity of phytochemicals found in the diet and the complexity of their biological effects. Recent progress in high-throughput analytical technologies and in bioinformatics now allows the simultaneous analysis of the hundreds or more metabolites constituting the metabolome in urine or plasma. These analyses give complex metabolic fingerprints characteristic of a given phenotype. The exploitation of the wealth of information it contains, in randomized controlled trials and cohort studies, should lead to the discovery of new markers of intake for phytochemicals and new markers of effects. In this paper, we briefly review the current methods used to evaluate intake of phytochemicals and their effects on health. We then describe the applications of metabolomics in this field. Recent metabolomics studies illustrate the potential of such a global approach to explore the complex relationships linking phytochemical intake and metabolism and health.

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Section: Metabolomics and Biomarker Discoverymentioning

confidence: 99%

The complex links between dietary phytochemicals and human health deciphered by metabolomics

Manach

Hubert

Llorach

et al. 2009

Molecular Nutrition Food Res

195

144

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“…The identification of a wide range of metabolites is done against a database of profiles of known purified compounds. To be able to find even small differences in metabolite composition between samples, different pattern recognition methods such as hierarchical cluster analysis and principal component analysis (Jurs 1986) can be applied to calculate an individual metabolite profile and compare it to other metabolite profiles. Profiles of samples which group into a defined cluster can then be used to define a "metabolic phenotype".…”

Section: Metabolomics -Profiling the Composition Of Metabolitesmentioning

confidence: 99%

Plant functional genomics

Holtorf

Guitton

Reski

2002

Naturwissenschaften

114

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Functional genome analysis of plants has entered the high-throughput stage. The complete genome information from key species such as Arabidopsis thaliana and rice is now available and will further boost the application of a range of new technologies to functional plant gene analysis. To broadly assign functions to unknown genes, different fast and multiparallel approaches are currently used and developed. These new technologies are based on known methods but are adapted and improved to accommodate for comprehensive, large-scale gene analysis, i.e. such techniques are novel in the sense that their design allows researchers to analyse many genes at the same time and at an unprecedented pace. Such methods allow analysis of the different constituents of the cell that help to deduce gene function, namely the transcripts, proteins and metabolites. Similarly the phenotypic variations of entire mutant collections can now be analysed in a much faster and more efficient way than before. The different methodologies have developed to form their own fields within the functional genomics technological platform and are termed transcriptomics, proteomics, metabolomics and phenomics. Gene function, however, cannot solely be inferred by using only one such approach. Rather, it is only by bringing together all the information collected by different functional genomic tools that one will be able to unequivocally assign functions to unknown plant genes. This review focuses on current technical developments and their impact on the field of plant functional genomics. The lower plant Physcomitrella is introduced as a new model system for gene function analysis, owing to its high rate of homologous recombination.

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“…"The eneral objective of pattern recognition stufiies is to predict an obscure property of an object (its origin or the class to which it belongs) on the basis of a set of indirect measurements" (Jurs, 1986). In our study, we needed to classify children as to the property of rowth faltering based upon ognition (Jurs, 1986) and was the method chosen here because graphical displays can retain a large amount of quantitative information in a way that can be absorbed (Cleveland, 1985). In articular, we used connected symbol graphs Yl ecause they are appropriate for time series when it is important to display both the pattern and the individual points unambiguously (Cleveland, 1985).…”

Section: Procedures For Classifying Children As To True Falteringmentioning

confidence: 99%

“…Another common feature of pattern recognition analysis is that measurements are normalized so that they can be more easil ization by plotting with each child's mea-B 7 wei ht or lengt a measurements. Graphical ana 7 ysis is one method used for pattern reccompared (Jurs, 1986 (WHO, 1983). These statistics were then used to generate plots showin the sex-specific tracks of growth at the me 8 ian and at 20.…”

Section: Procedures For Classifying Children As To True Falteringmentioning

confidence: 99%

Determining growth faltering with a tracking score

Frongillo

Rothe

Lambert

1990

American J Hum Biol

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A tracking score for determining growth faltering in children was developed and tested. A graphical method was developed for classifying by visual inspection whether or not a given child had faltered in growth. This method was used to classify all children in a sample of Guatemalan children as to faltering in both weight and length between 9 and 24 months of age; 80 of 345 children had faltered in weight, and 44 of 336 children had faltered in length. The accuracies for determining this faltering of seven versions of the tracking score and that of three other commonly used scores were assessed and compared using ROC curves, the areas under the curves, and two other ways. The other scores tested were the increment score, the residual score, and a growth-curve parameter score. The accuracies of the scores were overall lower for weight than for length. For both weight and length, the growth-curve parameter score had the lowest accuracy, and the tracking score that used an intermediate number of measurements (four in the interval of interest and three in the previous interval) had the highest accuracy. The tracking score has a number of features that make it attractive as an indicator of faltering. It is easy to calculate and interpret, preserves magnitude and direction, incorporates several growth measurements into a single indicator of faltering, gives equal weight to the information in all the measurements used, allows for missing measurements, and can be generalized to include other aspects of growth.

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Pattern Recognition Used to Investigate Multivariate Data in Analytical Chemistry

Cited by 85 publications

References 53 publications

The complex links between dietary phytochemicals and human health deciphered by metabolomics

The complex links between dietary phytochemicals and human health deciphered by metabolomics

Plant functional genomics

Determining growth faltering with a tracking score

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