All three projects in this Consortium require advanced biostatistics, informatics and data management techniques, both for data analysis and for tracking of results obtained with samples shared across projects. Led by Michael Bittner at TGen and Jeffrey Idle at the University of Bern, Core E will provide a variety of statistical and analytical support to ail three Projects, as well as to the Pilot Projects. Core E will also provide central, secure hosting for data exchange among the Consortium members. We will provide the following services for the three projects: 1. General statistical support for the three research projects. Core C (Irradiation) and the Pilot projects, in terms of experimental design and data analysis. Monte-Cario and non parametric approaches are commonly applied here. 2. Data viewing and analytical distribution testing to identify univariate trends among potential genomic or metabolomic biomarkers in cells as they react to radiation, to find independently informative, radiation damage biomarkers that may be used to gauge the level of severity of damage for a given individual exposed to a particular dose and type of radiation. 3. Multivariate analysis to identify groups of genomic or metabolomic biomarkers that act collaboratively to carry out the cellular response to radiation. 4. Random forests machine learning and self-organizing map analysis. 5. Contextual analysis to develop genomic or metabolomic biomarker panels that are specific for particular cell types, dose scenarios, or population subgroups. 6. A common secure data-hosting facility so that the considerable amount of data to be shared, due to the use of shared biological materials, can be readily exchanged. In summary. Core E will continue to provide the bioinformatics that will be crucial to the success of the CMCR program, and will provide statistical support in terms of experimental design and data analysis for all Consortium members.
The bioinformatics and statistical support provided by this Core will be necessary for the development of the most robust possible biodosimetry applications in each of the projects. The integration of data across projects has the further potential to provide mechanistic insight into radiation response and radiation sensitivity
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