Imaging/Bioinformatics Core

University Of California, San Francisco, San Francisco CA

Investigators

Linked publications & trials

Abstract

Cellular responses are heterogeneous, tissue specific, and a function of the history of a cell and its genome. In[unreadable] dealing with the heterogeneity of multiple model systems plus in-vivo studies, each proposed project will generate[unreadable] a large number of specimens for detailed quantitative and correlative analyses. The Imaging Bioinformatics Core[unreadable] will complement and extend the presently developed BioSig framework with two objectives: (1) to provide a fully[unreadable] annotated set of representative samples that are imaged at different resolutions, and (2) to populate databases[unreadable] that link anonymous patient data to mammography, breast density and expression profile data plus data obtained[unreadable] from histological analyses. For this objective annotation refers to user's input and feature-based representations[unreadable] that are computed using image analysis techniques. The first goal will target Projects 2, 3, and 4, and the second[unreadable] goal will target all Projects and Cores. Detailed quantitative representation of data enables comparative analysis[unreadable] of images based on their content, while linking data from different modalities enables event correlation and[unreadable] information visualization. Quantitative representation will be applied to (1) low-resolution compositional analysis of[unreadable] breast density, (2) low-resolution 3D modeling of ductal tree structures from regions of high and low breast[unreadable] density, (3) high-resolution 2D and 3D morphological and protein localization studies, and (4) analysis of[unreadable] expression profiles in support of Project 2. Compositional analysis will investigate the ratio of epithelial, stroma[unreadable] and adipose in low- and high-density regions. 3D representation of ductal tree structures enables comparative[unreadable] morphological analysis between different regions of breast tissue and quantitative analysis of high-resolution[unreadable] image data enables morphological and protein expression analysis using markers that target specific inter- and[unreadable] intracellular activities in tissue or cultured multicellular systems. The Core will couple user-defined annotations[unreadable] with the raw data and their computed annotations to (1) enable navigation between different data modalities, (2)[unreadable] provide graph-based queries, and (3) view the results through a Web-based interface in the form of plots, scatter[unreadable] diagrams, or images. This core enables sharing of data with collaborating investigators outside of the program[unreadable] project. The core will leverage the BioSig framework (developed at LBNL) and GeneTraffic platform (developed at[unreadable] lobion) in support of analysis of images through microscopy and microarray studies. The Core will extend the[unreadable] current ontology for managing radiological data, construct 3D models of the breast from Egan slices, and develop[unreadable] software tools to overlay gene expression and patterns of protein expression onto this 3D space for meaningful[unreadable] information visualization. The Core will enable navigation and query of this heterogeneous data space through[unreadable] graphical model, common schema, and controlled vocabulary. Quantitative representation of images and their[unreadable] annotation will be accessible to the BioStatistics Core for detailed sensitivity analysis.