UCSF Information and Cancer Commons: Part 1 of 2

June 10, 2022 3:00 p.m. - 4:00 p.m. ET

Join the upcoming NCI Containers and Workflows Interest Group (CWIG) webinar to learn about the research and functionality available through the University of California, San Francisco’s (UCSF) Information Commons.

As an open-source platform, the commons support deep data science and artificial intelligence methodologies. The platform offers access to de-identified clinical, imaging, and genomic profile data for 5.5 million UCSF patients. Drs. Sharat Israni and Gundolf Schenk will highlight examples of how leveraging this multidimensional data can result in richer scientific findings for cancer research.

At the next CWIG webinar in September, a practicing oncologist will present how they use this research data platform to conduct cancer research. 

CWIG is a monthly webinar series that brings together data scientists, bioinformaticians, computer scientists, and researchers to learn more about cloud computing and container technologies, workflows, and pipelines that could drive cancer data science.

The webinar series features a variety of presenters from across NIH, industry, and academia. Though cancer research is the focus of the series, unrelated data science and cloud computing topics are still welcome. In the last year, the CWIG webinar speakers have discussed:

  • NIH cloud programs like the Cancer Genomics Cloud, its fellow NCI Cloud Resources, and NIH STRIDES.
  • commercial cloud platforms for biomedical data storage and computing.
  • pipelines and tools for deep learning and various omics analysis.

This event is open to the public.

Sharat Israni, Ph.D.

Dr. Israni is the executive director and chief technology officer at UCSF’s Bakar Computational Health Sciences Institute, which is building the UCSF Information Commons.

Gundolf Schenk, Ph.D.

Dr. Schenk is a principal data scientist working at UCSF’s Bakar Computational Health Sciences Institute, where he applies his skills to integrate clinical notes and automate detection of structure within various modalities of biomedical data.

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