Tempus introduces Workspaces into its Lens Data Analytics Platform

Lens is trusted by nearly 100 institutions to uncover novel insights

Tempus, a leader in artificial intelligence and precision medicine, is broadly introducing its new Workspaces feature in the Lens platform. Workspaces is a private, coding-based cloud environment designed to unblock research and accelerate innovation. This new interface provides researchers a frictionless workflow for surfacing critical insights to power scientific discovery and development.

Lens is already trusted by nearly 100 institutions, including healthcare systems and biopharma companies, to power research that improves patient outcomes. Lens provides access to more than 200 petabytes of de-identified multimodal datasets, as well as a suite of tools that enable rapid time-to-insight, flexible analyses, and collaboration. In addition to Workspaces, other new features include the integration of Tempus One, an AI assistant that makes the vast amounts of data more accessible, and Cohort Builder, which helps define cohorts of interest by leveraging the platform’s rich molecular and clinical filters, including, cancer diagnosis, HLA genotype, comorbidities and more.

The new Workspaces feature enables users to easily leverage virtual machines designed to support a variety of research goals, query and analyze data directly in popular coding languages like python and R, and run tailored analyses using Tempus’s library of analytic templates. The flexibility it provides is the perfect complement to Lens’ other pre-built visualization tools that allow for faster time-to-insight and understand demographics, distribution, and prevalence across cohorts of interest.

“We’ve heavily invested in designing Lens to be the most seamless and fastest way to translate multimodal data into pivotal insights, and we’re proud that many institutions already rely on it for their research needs,” said Ryan Fukushima, Chief Operating Officer at Tempus. “Workspaces is flexible and fast, offering multiple virtual machine types to support diverse analytical needs and rapidly surface critical insights that can shape the future of oncology care.”

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