Translational Data Science
The Translational Data Science group collaborates with and supports both research and clinical staff to integrate effective data science and analysis into research and clinical care. Our goal is to create a feedback loop to foster a learning healthcare system.
We develop data products, data packages and data analysis tools to enable effective data use and integration into research and clinical care. We also collaborate with our clinical and research partners to develop statistical/ML/NLP models, data analyses, and data-driven dashboards and visualizations using real-world healthcare and laboratory-generated data.
Clinical Data Science
Our team supports clinical data science at Fred Hutch by analyzing real-world data generated over the course of health care delivery, and by improving the interoperability, quality, and accessibility of these data. They collaborate with Hutch researchers and staff to understand critical questions about cancer patients, develop impactful and practical data analyses, and to develop tools that support democratized data access, exploration, and analysis.
Research Informatics
Our team supports the use of biomedical datasets in the research context that often include large scale public or private genomic data, licensed or regulated datasets (such as those under DUAs or legal protection like GDPR), or laboratory-generated data that require significant computational processing as part of their analysis. We focus on providing open-source tooling, best practices for computational workloads and data management, and other resources such as computational workflows and templates that staff can use as a jumping off point to customize for their own work. We work closely with Fred Hutch IT’s Scientific Computing group who provide support for our on-prem computing cluster, scientific data storage, and research applications.
Workflow Support in WILDS
We’re developing resources in collaboration with the Fred Hutch IT Scientific Computing group to support simplified interfaces to computing resources through our project PROOF. This will facilitate users running WDL workflows on our cluster and you can find our emerging resources in our WILDS GitHub organization, such as our WDL supports and WILDS Docker Library.
Research Data Applications
We support the private Fred Hutch instance of cBioPortal in collaboration with Fred Hutch IT’s Scientific Computing group. cBioPortal allows users to easily explore multidimensional cancer genomics data with an intuitive point-and-click interface. For more information, read the SciWiki documentation and visit cbioportal.fredhutch.org to try it out.