UCB and Domino Data Lab Modernize Statistical Computing in Life Sciences

May 20, 2025
UCB and Domino Data Lab have announced a collaboration to modernize the Statistical Computing Environment for life sciences, integrating advanced cloud technologies and data science tools.

UCB and Domino Data Lab have announced a strategic collaboration to modernize the Statistical Computing Environment (SCE) in the life sciences sector, announced in a press release. This initiative aims to transform the legacy SCE into a unified, scalable, and flexible platform to meet the evolving demands of clinical research and regulatory compliance.

The collaboration will see the integration of diverse analytical tools such as SAS, R, and Python within a modern cloud-enabled environment. This platform will leverage Domino's expertise in cloud technologies and open-source support, combined with UCB's clinical research and regulatory knowledge. The goal is to enhance research capabilities and maintain compliance with industry standards like GxP, FDA 21 CFR Part 11, and GDPR.

The modernized SCE is expected to provide immediate benefits to researchers and data scientists by offering streamlined access to state-of-the-art tools. This enhancement aims to expedite the development of differentiated therapies, offering faster and more tailored treatment options for patients with severe diseases. Additionally, the integration of advanced data science will enable more efficient clinical studies, leveraging large-scale data analysis to improve predictions about efficacy and tolerability.

We hope you enjoyed this article

Consider subscribing to one of our newsletters like Life AI Weekly or Daily AI Brief.

Also, consider following us on social media:

Free newsletter

Life AI Weekly

Weekly coverage of AI applications in healthcare, drug development, biotechnology research, and genomics breakthroughs.

Whitepaper

Tensordyne Napier: What If One Rack Could Do the Work of Nine?

Tensordyne

This Tensordyne whitepaper presents Napier, an inference-focused AI processor and rack-scale system based on the company’s TDN Math logarithmic number system. It examines infrastructure requirements for large mixture-of-experts and agentic models, compares major inference architecture approaches, and details the TDN AIP processor, TDN72 pod, TDN Link fabric, and Napier Ultra configuration. The paper reports simulation-based performance, cost, and accuracy-validation results, including Tensordyne’s projected comparison of one Napier rack with a nine-rack Nvidia Rubin plus Groq deployment; the chip is reported as taped out and in fabrication.

Read more
Free, six days a week

Daily AI Brief: the AI news that matters, in your inbox.