Apr 06, 2011
from 03:30 PM to 04:30 PM
|Where||Brinkhous-Bullitt Building #219 (second floor)|
|Contact Name||John Kelley|
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Development of biomarkers that predict response to treatment and models that can direct development of new therapies requires integration of many complementary types of biomedical information captured at multiple scales. In the context of our caBIG® In Silico Brain Tumor Research Center, we are developing methodologies, information models, tools, and analytic pipelines that will make it feasible to systematically carry out large-scale integrative analyses of: 1) whole slide digital pathology and radiology based features, and 2) deep-sequencing data and patterns of protein and gene expression. The methods and tools will be designed to carry out the following closely interrelated tasks: 1) systematically manage, query and analyze results produced by data analyses composed of large numbers of interrelated algorithms, 2) compare results produced by workflows consisting of cascades of multiple algorithms, 3) efficiently manage result datasets that in aggregate will contain trillions of imaging derived features, 4) engage human neuropathologists and radiologists in validation of results and motivation of new analyses, and 5) support histological feature query and analysis patterns needed to link histological features with “omic” and outcome data.