With the Flywheel platform in place, Roche and Genentech researchers can access high-quality images for complex analysis and machine learning, ultimately speeding the development of innovative therapies.
Flywheel, the leading cloud-scale informatics platform for biomedical research and collaboration, announced today the successful integration of its platform by Roche and Genentech, a member of the Roche Group, for ingestion, classification, standardization, curation and analysis of medical imaging data.
@Flywheel_io, @Roche and @genentech team up to speed the development of innovative therapies. See how.Tweet this
The secure, scalable Flywheel platform enables aggregation and management of medical imaging and associated data to accelerate drug discovery. The data is organized and processed with automated pipelines—saving significant time and minimizing the potential for human error in the drug development process. Cost and timeline efficiencies allow researchers to focus on what matters most—bringing life-changing therapies to patients faster. Moreover, Flywheel’s unique approach has enabled multi-site collaboration and development of a customized solution for Roche and Genentech’s needs.
“Manual processes to curate data at the terabyte and petabyte levels are historically costly, time-consuming and prone to human error. With our platform in place, Roche and Genentech researchers can access high-quality images for complex analysis and machine learning, ultimately speeding the development of innovative therapies,” said Jim Olson, CEO of Flywheel. “Before using the platform, this level of collaboration and analysis was simply not possible.”
“At Roche we envision a future where data, analytics and digital technologies routinely enable more targeted, efficient research and development and more integrated, personalized care,” said James Sabry, Head of Roche Pharma Partnering. “The Flywheel platform enables rapid access to highly-curated imaging data, enhancing our ability to answer key scientific questions that are critical to advancing better patient care experiences and outcomes.”
Life sciences organizations are making significant investments in digital transformations that foster AI technology in hopes of improving R&D processes and bringing drugs to the market faster. Modern infrastructures are needed to aggregate, curate, and analyze a vast assortment of rich, biomedical data to support these initiatives and enable machine learning, big data analytics, and other strategic data-driven objectives.