The overall goal of the Administrative Core is to function as the integrative unit of the DTSGC to enable the close coordination of the functions of the other Cores: Data Generation Core (DGC), the Data Analysis & Signature Development Core (SGC) and the Community Interactions and Outreach Core (CIO). A complex project such as the DTSGC requires nimble and proactive management approaches that merge good scientific practices with good management principles to streamline workflow. The Administrative Core will have mechanisms in place to deal with new and sometime unanticipated challenges so that workflow is efficient in an ongoing manner. Efficiency in workflow will be to be closely coupled to quality assurance and quality control, and a constant commitment to quality and adherence to SOPs will permeate all facets of the DTSGC. Enabling QA/QC will be a central focus of the CAC. The Administrative Core will ensure that Data and signature generation is well-integrated with data release and community interactions. This integration needs to be continually nurtured and managed. The CAC will provide the administrative support to enable smooth flow of DGC and SGC outputs to CIO core for dissemination. There are likely to be unanticipated challenges both scientific and procedural as the project proceeds. Through ongoing monitoring and analyses and use of process engineering protocols the CAC will identify weak points in the data to signature generation supply chain to enable change in protocols to account for unanticipated scientific issues, f increase in process efficiency and mid-course corrections resulting from advances in technologies. Overall the Administrative Core will function as the glue that holds all parts of the DTSGC together.

National Institute of Health (NIH)
National Human Genome Research Institute (NHGRI)
Specialized Center--Cooperative Agreements (U54)
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Special Emphasis Panel (ZRG1-CB-D (50))
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Ajay, Ajay
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Icahn School of Medicine at Mount Sinai
New York
United States
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Stern, Alan D; Rahman, Adeeb H; Birtwistle, Marc R (2016) Cell size assays for mass cytometry. Cytometry A :
Smith, Gregory R; Birtwistle, Marc R (2016) A Mechanistic Beta-Binomial Probability Model for mRNA Sequencing Data. PLoS One 11:e0157828
Gallo, James M; Birtwistle, Marc R (2015) Network pharmacodynamic models for customized cancer therapy. Wiley Interdiscip Rev Syst Biol Med 7:243-51
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