FEB 24, 2016 12:00 PM PST

G-DOC Plus: A Data Science Platform for Precision Medicine Research

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  • Director of the Innovation Center for Biomedical Informatics (ICBI) and Associate Professor of Oncology, Georgetown University Medical Center
      Dr. Subha Madhavan is Director of the Innovation Center for Biomedical Informatics (ICBI) at the Georgetown University Medical Center and Associate Professor of Oncology. She is a world-class leader in data science, clinical informatics and health IT who is responsible for several biomedical informatics efforts including the software development of Georgetown Database of Cancer (G-DOC) a resource for both researchers and clinicians to realize the goals of personalized medicine and co-directs Lombardi Cancer Center's Biostatistics and Bioinformatics shared resource.

      In her role as the CTSA biomedical informatics director, she has enabled access to over 2.5 million patient records from 10 MedStar Health hospitals to translational researchers. She was the PI on the Breast and Colon Cancer Family Registries data center that coordinates public health and epidemiology data across 12 sites in the US, Australia, and Canada. More recently, she has partnered with the FDA on the Center for Excellence in Regulatory Science program to develop evidence bases for pharmacogenomics and vaccine safety. She collaborated with the Inova translational medicine institute to help manage 1000's of patient genomes on the Amazon cloud to facilitate large-scale statistical analysis and genotype-phenotype association testing. She has contributed to novel information sciences findings in research articles published in journals such as Nature, Bioinformatics, Molecular and Cell Biology (MCB), AJPM, Frontiers in Oncology, Bioinformatics, Cancer Informatics, and Molecular Cancer Research (MCR).

      Dr. Madhavan has a Master's degree in Information Technology from University of Maryland and a Ph.D. in Molecular Biology and Biological Sciences from the Uniformed Services University for the Health Sciences through a highly ranked Indo-US Collaborative program.


    The advent of the microarray technology in 2000 has paved the way for advanced translational research methods that use molecular markers such as microRNA, proteins, metabolites and copy number data. The popularity of next generation sequencing (NGS) grew exponentially in 2007 when a faster, more accurate and affordable sequencing throughput became a reality. Since then, the size and complexity of genomic data has increased many fold, making its analysis, management and integration increasingly challenging. To drive hypothesis generation and validation of molecular markers for biologists and researchers, it would be convenient to have a “one–stop” system that can handle omics and de-identified clinical data in one location.In response to this need, we have developed a software research platform called G-DOC Plus. G-DOC Plus is an enhanced web platform that uses cloud computing and other advanced computational tools to handle NGS and medical images so that they can be analyzed in the full context of other omics and clinical information to drive personalized medicine research. G-DOC Plus tools have been leveraged in the cancer and non-cancer realms for hypothesis generation in precision medicine and translational research.  With the goal of improving overall health outcomes through advanced genomics research, G-DOC Plus, enables the integrative analysis of multiple a variety of data types to understand mechanisms of cancer and non-cancer diseases to drive new hypothesis for precision medicine.  G-DOC Plus allows researchers to explore data one patient at a time, as a sub-cohort of samples; or as a population as a whole, providing the user with a comprehensive view of the data.

    Learning Objectives: 

    1. Learn how to use a publicly available translational research software platform
    2. Learn how to combine omics and clinical data to generate novel hypothesis for cancer studies
    3. Learn about application of Bioinformatics to discover prognostic biomarkers in stage II colorectal cancers

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