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Genomic Data Sharing

  • ID: 5018854
  • Book
  • October 2020
  • Region: Global
  • 360 Pages
  • Elsevier Science and Technology
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Genomic Data Sharing: Case Studies, Challenges, and Opportunities for Precision Medicine provides a comprehensive overview of current and emerging issues in genomic data sharing. In this book, international leaders in genomic data examine these issues in-depth, offering practical case studies that highlight key successes, challenges and opportunities. Sections discuss the eMERGE Network, Undiagnosed Disease Network, Vanderbilt Biobank, Marshfield Clinic Biobank, Minnesota Authorization, Rochester Epidemiology Project, NIH sponsored biobanks, GINA, and Global Alliance for Genomics and Health (GA4GH). In addition to these perspectives from the frontlines, the book also provides succinct overviews of ethical, legal, social and IT challenges.

Clinician investigators, clinicians affiliated with academic medical centers, policymakers and regulators will also gain insights that will allow them to navigate the increasingly complex ethical, social and clinical landscape of genomic data sharing.

  • Covers both technical and ELSI (ethical, legal, and social implications) perspectives on genomic data sharing
  • Includes applied case studies of existing genomic data sharing consortia, including the eMERGE Network, Undiagnosed Disease Network, and the Global Alliance for Genomics and Health (GA4GH), among others
  • Features chapter contributions from international leaders in genomic data sharing
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1. Introduction 2. Consortium example: eMERGE Network 3. Consortium example: Undiagnosed Disease Network 4. Biobank example: Vanderbilt 5. Biobank example: Marshfield Clinic 6. State Law: Minnesota Authorization and the Rochester Epidemiology Project 7. US Regulatory frameworks: Convergence of HIPAA and NIH Data sharing Policy 8. US Regulatory frameworks: GINA 9. An international alliance: Global Alliance for Genomics and Health (GA4GH) 10. Security: Whose perspective matters? 11. Data governance: What does it mean and who are the stakeholders? 12. Concluding Chapter

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Mccormick, Jennifer B.
Dr. McCormick is an interdisciplinary academic having completed a doctorate degree in molecular and cellular biology, postdoctoral fellowship in biological chemistry, masters' degree in public policy, and NIH Center of Excellence in ELSI Research fellowship. She conducts empirical studies examining the policy implications and ethical challenges of translating research into clinical care and public health. Much of her work focuses on the ethical, legal, political, and social implications of medical record and genomic data sharing, the challenges to protecting participants' privacy and confidentiality in the era of 'big data', and the ethical complexities presented by translating genomic research findings into clinical and public health domains. She has also been involved in initiatives aimed at enhancing human participation in research and promoting professionalism and social responsibility in biomedical research. She lectures frequently on topics related to research and translational research ethics, translational genomics, and social responsibility and policy. She has published on topics related to research ethics consultation, genetic and genomic research and biobanking, human research participant engagement and protection, and challenges in translational research. Beyond Sputnik: US Science Policy in the 21st Century (Neal, Smith, and McCormick) is considered one of the first general textbooks on national science policy and is used in science policy training and fellowship programs.
Pathak, Jyotishman
Dr. Pathak is the Frances & John L. Loeb Professor of Medical Informatics and the Chief of Division of Health Informatics at Weill Cornell Medicine, Cornell University, New York. Prior to joining Weill Cornell, he was the Professor of Biomedical Informatics at Mayo Clinic in Rochester, Minnesota (2007-2015) where he led two major NIH/HHS funded initiatives-the Electronic Medical Records and Genomics (eMERGE) and Strategic Health IT Research Project (SHARP) projects-which have pioneered techniques for high-throughput phenotyping from the electronic medical record. His research interests and expertise lie in developing and applying informatics methods for data mining and phenotype extraction from electronic medical records (EMRs), and their applications in pharmacogenomics, comparative effectiveness research, and population health research, particularly focusing on mental health disorders. Throughout his career, he has led and collaborated across multiple investigators in several NIH/DHHS consortiums, including, most recently at Mayo, as the Co-PI of eMERGE and PI of PCORI Learning Health Systems Clinical Data Research Network, and currently at Weill Cornell, as the Co-PI for the PCORI New York City Clinical Data Research Network (NYC-CDRN) and the PI for NIH Big Data to Knowledge (BD2K) R25 Training and Education Program on Biomedical Informatics (BD2BMI).
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