The GSP Analysis Centers co-organized the 2018 Harvard Program in Quantitative Genomics Conference on Biobanks: Study Design and Data Analysis. See https://www.hsph.harvard.edu/2018-pqg-conference/. Below is the conference announcement. The GSP investigators are welcome to attend it if interested.
Biobanks: Study Design and Data Analysis
November 1-2, 2018
Harvard Medical School Conference Center | Boston, MA
The impetus for the 2018 Conference of the Program in Quantitative Genomics of Harvard T.H. Chan School of Public Health comes from the proliferation of large scale biobanks worldwide. Biobanks are composed of massive genetic and genomic data, epidemiological data, Electronic Medical Records, wearable devices and imaging data. Examples of large biobanks include UK Biobank, China Kadoorie Biobank, eMERGE, Finland Biobank, Million Veteran Program, and MyCode Project of the Geisinger Health System, among others. The use of biobanks is becoming an essential and potentially revolutionary approach to biomedical research, with the capacity to improve the prevention, diagnosis, and treatment of a wide range of illnesses, and to advance personalized health. To take full advantage of the enormous opportunities presented in biobanks, there is an urgent need for discussing important quantitative issues, leveraging interdisciplinary expertise, and designing studies with increased scale and power. This conference aims at discussing several key quantitative challenges in biobank studies, including designing biobanks to meet a wide array of needs, developing strategies for improving phenotyping accuracy, harmonizing data across biobanks, and developing analytic methods for biobank data.
The conference will be centered on the following three topics:
SESSION 1: Types of Biobanks
SESSION 2: Biobank Data Analysis
SESSION 3: Phenotyping and Harmonization Across Biobanks
Tianxi Cai Harvard T.H. Chan School of Public Health
Gil McVean Oxford University
Catherine Sudlow UK Biobank, University of Edinburgh
Zhengming Chen China Kadoorie Biobank, Oxford University
Kelly Cho Brigham and Women’s Hospital, Harvard Medical School Joshua Denny Vanderbilt University
David Ledbettter Geisinger Health System
Seunggeun Shawn Lee University of Michigan
Shawn Murphy Massachusetts General Hospital, Harvard Medical School Benjamin Neale Massachusetts General Hospital, Harvard Medical School Samuli Ripatti University of Helsinki, Institute for Molecular Medicine Finland (FIMM)
Manuel Rivas Stanford University
The conference program includes time for scientific presentations and a poster session and reception for submitted abstracts. Please visit the conference website for more details.
Registration + travel awards will be provided to support junior researchers who submit abstracts.
*Three abstracts will be selected to be presented as 10-minute platform talks
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UC Irvine’s Center for Complex Biological Systems is pleased to announce the annual short course in Big Data Image Processing & Analysis (BigDIPA), September 17-21, 2018.
This 1-week workshop course is geared towards graduate students, postdocs, faculty and industry professionals with research interests in navigating, manipulating and extracting information from “Big Data” image sources. The course is designed to cover the complete “vertical integration” of the image data to knowledge pipeline.
The course will provide a mix of strategies for dealing with biological/biomedical big data image sources, using examples of image analyses drawn from advanced cell fluorescence microscopy techniques and neurobiology to highlight fundamental concepts and skills. Processing and analysis techniques will be generalizable and relevant to other model systems and biomedical input data sources.
For more information and to apply please visit: http://bigdipa.ccbs.uci.edu
a useful resource on data visualization.
Most chapters discuss obvious things, but chapter 13, 14 is very useful. http://serialmentor.com/dataviz/
Related to earlier PLOS effort. Something to think about…