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Precise Classification (precise + classification)

Distribution by Scientific Domains

Medical Sciences	50%

Selected Abstracts

Bayesian classification of tumours by using gene expression data

JOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES B (STATISTICAL METHODOLOGY), Issue 2 2005
Bani K. Mallick
Summary., Precise classification of tumours is critical for the diagnosis and treatment of cancer. Diagnostic pathology has traditionally relied on macroscopic and microscopic histology and tumour morphology as the basis for the classification of tumours. Current classification frameworks, however, cannot discriminate between tumours with similar histopathologic features, which vary in clinical course and in response to treatment. In recent years, there has been a move towards the use of complementary deoxyribonucleic acid microarrays for the classi-fication of tumours. These high throughput assays provide relative messenger ribonucleic acid expression measurements simultaneously for thousands of genes. A key statistical task is to perform classification via different expression patterns. Gene expression profiles may offer more information than classical morphology and may provide an alternative to classical tumour diagnosis schemes. The paper considers several Bayesian classification methods based on reproducing kernel Hilbert spaces for the analysis of microarray data. We consider the logistic likelihood as well as likelihoods related to support vector machine models. It is shown through simulation and examples that support vector machine models with multiple shrinkage parameters produce fewer misclassification errors than several existing classical methods as well as Bayesian methods based on the logistic likelihood or those involving only one shrinkage parameter. [source]

,Green earths': vibrational and elemental characterization of glauconites, celadonites and historical pigments

JOURNAL OF RAMAN SPECTROSCOPY, Issue 8 2008
Francesca Ospitali
Abstract ,Green earths' are employed since antiquity as pigments in the creation of artworks. The minerals responsible for the colour belong to four groups: (1) the clayey micas celadonite and glauconite, undoubtedly the most common; (2) smectites; (3) chlorites; (4) serpentines. Whereas there have been several studies on clayey materials, mineralogical analyses in the field of cultural heritage are mainly limited to the identification of the green earth without specific characterization of the mineralogical species. This work shows a preliminary characterization by the multi-techniques approach of some raw minerals (glauconite, celadonite and ferroceladonite). Vibrational analyses have been correlated with elemental analyses, thanks to the hyphenated instrumentation of scanning electron microscopy with EDS and Raman structural and chemical analyser (SEM-EDS-SCA) probes, which permitted collection of EDS and Raman spectra on the same microscopic area. Micro-Raman and Fourier transform infrared attenuated total reflectance (FTIR-ATR) spectroscopies were able to distinguish between celadonite and glauconite. The use of different lasers revealed resonance effects in the Raman spectra. In addition to pure minerals, archaeological samples and commercial green earths were also analysed, thereby enabling a more precise classification of the green pigments in heterogeneous samples such as wall paintings. Some commercially available green earths were found to contain organic dyes. Copyright © 2008 John Wiley & Sons, Ltd. [source]

The ratio of serum transferrin receptor and serum ferritin in the diagnosis of iron status

BRITISH JOURNAL OF HAEMATOLOGY, Issue 1 2001
Babatyi I. Malope
Laboratory tests used in the diagnosis of iron status lack specificity in defining iron deficiency anaemia (IDA) and anaemia of inflammation (AI). The serum transferrin receptor (sTfR) may provide more information in this regard. The iron status of 561 pre-school children was determined and classified using the conventional measurements. The value of the concentration of sTfR, the ratio of sTfR (µg/ml) to LogSF (µg/l) (TfR-Index), and the Log of the ratio of sTfR (µg/l) to SF (µg/l) , (LogTfR:Fer ratio), in the classification of the iron status were determined by comparing their distributions across the classification of iron status. Although there were significant differences in sTfR and TfR-Index across the categories of iron status, there was considerable overlap. All subjects with iron deficiency had LogTfR:Fer ratio >,2·55, whereas in all subjects classified as AI it was < 2·55, thus clearly separating the two. The LogTfR:Fer ratio was not able to exclude IDA in the presence of inflammation. However, in cases of combined IDA and AI the LogTfR:Fer ratio was <,2·55 but increased to >,2·55 after resolution of the inflammation. This novel method of calculating the LogTfR:Fer ratio may provide a more precise classification of the iron status of children. [source]

Multi-dimensional phenotyping: towards a new taxonomy for airway disease

CLINICAL & EXPERIMENTAL ALLERGY, Issue 10 2005
A. J. Wardlaw
Summary All the real knowledge which we possess, depends on methods by which we distinguish the similar from the dissimilar. The greater the number of natural distinctions this method comprehends the clearer becomes our idea of things. The more numerous the objects which employ our attention the more difficult it becomes to form such a method and the more necessary. [1]. Classification is a fundamental part of medicine. Diseases are often categorized according to pre-20th century descriptions and concepts of disease based on symptoms, signs and functional abnormalities rather than on underlying pathogenesis. Where the aetiology of disease has been revealed (for example in the infectious diseases) a more precise classification has become possible, but in the chronic inflammatory diseases, and in the inflammatory airway diseases in particular, where pathogenesis has been stubbornly difficult to elucidate, we still use broad descriptive terms such as asthma and chronic obstructive pulmonary disease, which defy precise definition because they encompass a wide spectrum of presentations and physiological and cellular abnormalities. It is our contention that these broad-brush terms have outlived their usefulness and that we should be looking to create a new taxonomy of airway disease,a taxonomy that more closely reflects the spectrum of phenotypes that are encompassed within the term airway inflammatory diseases, and that gives full recognition to late 20th and 21st century insights into the disordered physiology and cell biology that characterizes these conditions in the expectation that these will map more closely to both aetiology and response to treatment. Development of this taxonomy will require a much more complete and sophisticated correlation of the many variables that make up a condition than has been usual to employ in an approach that encompasses multi-dimensional phenotyping and uses complex statistical tools such as cluster analysis. [source]