GNS Healthcare (GNS), a precision medicine company that applies causal machine learning technology to massive and diverse data streams to better match drugs and other health interventions to individual patients, has announced that Celgene Corporation has entered into a service and license arrangement for the rights to operate the GNS Healthcare REFS™ (Reverse Engineering and Forward Simulation) causal machine learning and simulation platform for applications across drug discovery, clinical development, and commercialization and market access. In addition, several GNS causal modeling experts will be brought in-house at Celgene sites to operate the platform. GNS also announced that Celgene has made a second equity investment in GNS.
This service and license arrangement with embedded GNS employees is a linking of people, process and technology. It provides Celgene leaders with the flexibility and autonomy to determine priorities and specific applications of causal machine learning and “what if?” simulation, potentially transforming strategic data assets into causal computer models to help support decisions across multiple therapeutic areas and throughout the product life cycle.
The GNS Healthcare REFS platform is designed to rapidly answer complex questions in healthcare. REFS routinely leverages combinations of genomic, molecular, clinical, pharmacy and medical claims, EMR, emerging real-world, and other types of data to uncover causal mechanisms between variables to answer questions that can only be answered by causal machine learning.
“As our industry seeks answers from increasingly rich and complex data sources, GNS has begun to offer partners the ability to bring the power of causal machine learning inside their own organizations and gain direct access to the REFS platform and our causal modeling experts,” said Iya Khalil, Chief Commercial Officer, Executive Vice President and Co-Founder of GNS. “Companies that embrace data-driven frameworks and culture such as Celgene are gaining a competitive advantage to rapidly generate insights that are simply not possible with any other analytics methodology.”