Poster abstracts 72 ically organize them in biologically meaningful ways. There are many options for the implementation of feature extraction and gene clustering programs to help accomplish these goals. We have developed Matlab prototypes that emphasize flexibility, accuracy, transparency and the systematic incorporation of statistical analysis. These prototypes facilitate the development and refinement of algorithms as we converge on fully automatic analysis.There are many possible approaches to feature extraction, and the choices are influenced by factors including: feature morphology and uniformity, and positioning errors, array deposition methodologies and tolerances, labelling methods and scanner specifications. Our feature extraction software includes several methodologies and a number of adjustable parameters that can be modified to suit the application. For example, image file formats, image alignment procedures for two-colour images, irregular modifiable grids, feature locating, grid fitting, extraction, pixel-level outlier rejection, feature-level outlier rejection, background-subtraction and dye-normalization processes have all been incorporated. With this feature extraction prototype, we are can explore different methods of image analysis, outlier rejection, background subtraction and normalization while optimizing sensitivity and specificity. Relative expression levels are reported along with their respective statistical information.To analyse data from multiple array experiments we developed a package built around a clustering method based on a graph theoretic approach. This approach requires no assumptions as to the number of clusters expected and is flexible with regard to the selection of a similarity function. In particular, similarity functions that take into account statistical characteristics, such as confidence intervals, are supported. Results from applying both analysis tools to raw images and to processed gene expression data from humans, C. elegans and other organisms will be presented.The metabolic overlap syndromes hyperlipidaemia, diabetes mellitus, insulin resistance, obesity and hypertension together constitute the major risk factors for coronary heart disease. The genetic predisposition to these conditions is strong but complex, and many of the genes involved have not been identified. The identification of the genes or metabolic pathways involved in these disorders would lead to new diagnostic and therapeutic targets with wide applicability. The spontaneously hypertensive rat (SHR) is a model of essential hypertension which also displays abnormalities of lipid metabolism and insulin resistance similar to those found in the human metabolic syndromes. These include raised blood triglycerides and fatty acids, defective catecholamine-mediated lipolysis and excessive growth of intra-abdominal adipocytes. Similar abnormalities have been identified in combined hyperlipidaemia, maturity-onset diabetes mellitus and obesity in humans. We are using the SHR as a model to identify genes and pat...
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