Cellular responses are heterogeneous, tissue specific, and a function of the history of a cell and its genome. Indealing with the heterogeneity of multiple model systems plus in-vivo studies, each proposed project will generatea large number of specimens for detailed quantitative and correlative analyses. The Imaging Bioinformatics Corewill complement and extend the presently developed BioSig framework with two objectives: (1) to provide a fullyannotated set of representative samples that are imaged at different resolutions, and (2) to populate databasesthat link anonymous patient data to mammography, breast density and expression profile data plus data obtainedfrom histological analyses. For this objective annotation refers to user's input and feature-based representationsthat are computed using image analysis techniques. The first goal will target Projects 2, 3, and 4, and the secondgoal will target all Projects and Cores. Detailed quantitative representation of data enables comparative analysisof images based on their content, while linking data from different modalities enables event correlation andinformation visualization. Quantitative representation will be applied to (1) low-resolution compositional analysis ofbreast density, (2) low-resolution 3D modeling of ductal tree structures from regions of high and low breastdensity, (3) high-resolution 2D and 3D morphological and protein localization studies, and (4) analysis ofexpression profiles in support of Project 2. Compositional analysis will investigate the ratio of epithelial, stromaand adipose in low- and high-density regions. 3D representation of ductal tree structures enables comparativemorphological analysis between different regions of breast tissue and quantitative analysis of high-resolutionimage data enables morphological and protein expression analysis using markers that target specific inter- andintracellular activities in tissue or cultured multicellular systems. The Core will couple user-defined annotationswith the raw data and their computed annotations to (1) enable navigation between different data modalities, (2)provide graph-based queries, and (3) view the results through a Web-based interface in the form of plots, scatterdiagrams, or images. This core enables sharing of data with collaborating investigators outside of the programproject. The core will leverage the BioSig framework (developed at LBNL) and GeneTraffic platform (developed atlobion) in support of analysis of images through microscopy and microarray studies. The Core will extend thecurrent ontology for managing radiological data, construct 3D models of the breast from Egan slices, and developsoftware tools to overlay gene expression and patterns of protein expression onto this 3D space for meaningfulinformation visualization. The Core will enable navigation and query of this heterogeneous data space throughgraphical model, common schema, and controlled vocabulary. Quantitative representation of images and theirannotation will be accessible to the BioStatistics Core for detailed sensitivity analysis.
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