Additional Videos


  • Keynote Speaker: Christopher A. Longhurst, MD, MS

    "Big Data" is an overhyped term in biomedicine. In this talk, Dr. Longhurst will share some real-world examples of the value realized by bringing big data to the bedside in both clinical research at Stanford and commercial startups in the Silicon Valley.

  • Keynote Speaker: Jessica Tenebaum, PhD

    The past decade has seen the emergence and expansion of the new discipline of translational bioinformatics (TBI). The field of TBI centers around the development of novel methods to transform increasingly voluminous amounts of molecular and biomedical data into improved human health.

  • Data Analysis

    The computational and statistical toolkit for quantitative analysis of large data sets is developing rapidly, partly through the focus on “Big Data”.

  • Data Application

    The successful application of informatics requires understanding of data, technology and the unique needs of the beneficiary, whether the consumer of food products or a critically ill patient.

  • Image Analysis

    Images are used to store vast amounts of biological and clinical data. Increasingly powerful informatics methods to detect subtle variations in image data are utilized in a wide variety of disciplines and models.

  • Data Visualization

    Data visualization is increasingly important for hypothesis generation, exploratory research and qualitative analysis. Emerging technologies that generate highly visual representations of data can foster recognition of novel associations.

  • Data Structure

    Alignment of data structure with anticipated applications is important at all levels of bioinformatics and has major influence on the success of analysis and other applications.

  • Data Standardization & Integration

    Integrating disparate data sources, whether for clinical or biological research, requires effective data standardization. Likewise, preparing data for translation from basic research settings into clinical applications requires understanding of the terminologies and standards used in each venue.

  • System Strategies

    Systems analysis requires managing multiple scales and disparate data sources. Strategies for sharing data across non-affiliated systems and for analyzing biological data using biological pathways will be shared.