AUG 21, 2014 11:30 AM PDT

Understanding the Genetics of Common Disease: Using Big Data approaches to see the emergent whole

C.E. CREDITS: CE | CEU
Speakers
  • Director of Computational Sciences and Informatics program for Complex Adaptive Systems and Professor in the School of Life Sciences, Arizona State University
    Biography
      Dr. Ken Buetow is a human genetics and genomics researcher who leverages computational tools to understand complex traits such as cancer, liver disease, and obesity. Dr. Buetow currently serves as Director of Computational Sciences and Informatics program for Complex Adaptive Systems at Arizona State University (CAS@ASU) and is a professor in the School of Life Sciences in ASU's College of Liberal Arts and Sciences. CAS@ASU applies systems approaches that leverage ASU's interdisciplinary research strengths to address complex global challenges. The Computational Sciences and Informatics program is developing and applying information technology to collect, connect, and enhance trans-disciplinary knowledge both within ASU and across the broader knowledge-generating ecosystems. CAS@ASU is creating a Next Generation Cyber Capability to address the challenges and opportunities afforded by "Big Data" and the emergence of 4th Paradigm Data Science. This capability brings state-of-the-art computational approaches to CAS@ASU's transdisciplinary, use-inspired research efforts Dr. Buetow previously served as the Director of the Center for Biomedical Informatics and Information Technology within the National Institutes of Health's National Cancer Institute (NCI). In that capacity he initiated and oversaw the NCI's efforts to connect the global cancer community through community-developed, standards-based, interoperable informatics capabilities that enable secure exchange and use of biomedical data. Buetow designed and built one of the largest biomedical computing efforts in the world. He was responsible for coordinating biomedical informatics and information technology at the NCI. The NCI center he led focused on speeding scientific discovery and facilitated translational research by coordinating, developing and deploying biomedical informatics systems, infrastructure, tools and data in support of NCI research initiatives.

    Abstract:

    The comprehensive, multidimensional molecular characterization of tumors and the individuals in which they have developed is transforming cancer definition, diagnosis, treatment, and prevention. These technologies identify the millions of variants present in normal individuals and thousands of alterations that occur during the course of tumor development. This systems-wide molecular analysis has identified a complex cacophony of inherited and acquired variation. The integration and interpretation of this complex multidimensional information into evidence exceeds raw human cognitive capacity. It presents challenges of contextualizing the data and converting it into actionable information.

    Data Science has the capacity to provide the needed tools to tackle this challenge. Arizona State Universitys (ASU) Complex Adaptive Systems team is building a first generation Data Science research platform - the Next Generation Cyber Capability (NGCC). The ASU NGCC composed of hardware, software, and people transforms Big Data to information and creates the evidence necessary to enable personalized medicine. The NGCC permits data points to be evaluated in concert using Big Data analytic frameworks thereby identifying an emergent, coherent whole. Biologic network analysis represents one such promising integrative approach. These networks account for the individual heterogeneity in underlying etiology as well as the interaction of diverse events necessary to generate a complex phenotype such as cancer. Emerging collections of analytic approaches permit analysis using genome-wide data sets and established biologic networks as models.

    These approaches are being applied to understand the origins and outcomes of cancer. Big Data approaches are identifying key biologic processes underpinning cancer susceptibility and oncogenesis. Novel analytic approaches are being applied to identify new strategies for intervention.


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