New Genedata Imagence 2.0 Automates and Catalyzes HCS Image Analysis
Deep learning-based Genedata Imagence sets new benchmarks for automation, simplifying and accelerating HCS for Biopharma R&D
May 19, 2020
Genedata, the leading provider of enterprise software solutions for biopharmaceutical R&D, today announced the release of Genedata Imagence® 2.0, the latest version of its award-winning enterprise software for high-content screening (HCS) image analysis. Leading biopharmaceutical companies are rapidly adopting Genedata Imagence for speedier development and broader roll-out of imaging assays in their R&D workflows, leveraging it as the first enterprise solution to automate the analysis of HCS images with deep learning.
Genedata Imagence 2.0 sets new benchmarks for HCS. It now offers full end-to-end automation, reducing the time spent on image analysis by an order of magnitude. Allowing incremental learning and knowledge preservation, it enables the analysis of complex phenotypic assay formats without extensive expert input, and the transfer of gained knowledge to new experimental setups when meaningful, thus advancing HCS assays into production much more quickly than with classical analysis pipelines.
This latest version of Genedata Imagence has been further optimized for use of distributed computing resources, allowing computing power to be scaled up when needed; in particular, the parallelization of image analysis steps results in massive time and cost savings. Genedata Imagence 2.0 is also fully interoperable with Genedata Screener® for HCS. Similar to other components of the Genedata Biopharma Platform, it can be deployed using on-demand cloud computing service providers like Amazon Web Services (AWS).
Genedata is continuously extending its partnerships with innovative technology providers through its Genedata Ready-to-Run™ program for full compatibility and integration between the Genedata Biopharma Platform and laboratory instruments and robots. Several Ready-to-Run partners, including Molecular Devices, now offer their technology optimized for use with Genedata Imagence.
“As a world-leading provider of high-performance automated imaging solutions, we are excited to offer deep learning data-rich answers with advanced image-based feature extraction to our customers,” said Susan Murphy, President of Molecular Devices. “From the perspective of being an instrument manufacturer, Genedata’s Ready-to-Run program has allowed additional image analysis capabilities we otherwise would not have been able to offer. Our ImageXpress® Micro high-content imaging system customers can now easily implement walk-away, automated image analysis workflows, while gaining deep insights with Genedata’s Imagence and Screener software in much less time.”
Othmar Pfannes, Ph.D., CEO of Genedata, said “We are excited about the latest release of our highly innovative Genedata Imagence platform, which has already won several awards for the value it brings to biopharma R&D. Acting on the feedback we have received from some of our users, we have now further improved the workflow automation and usability of the software.” Dr. Pfannes continued, “We are also delighted that innovative technology providers such as Molecular Devices have recognized the value of partnering with Genedata and look forward to extending our partnership further to the benefit of our joint customers in the biopharma industry.”
Genedata transforms data into intelligence with innovative software solutions that incorporate extensive biopharma R&D domain knowledge. Multinational biopharmaceutical organizations and cutting-edge biotechs around the globe rely on Genedata to digitalize and automate data-rich and complex R&D processes. From early discovery all the way to the clinic, Genedata solutions help maximize the ROI in R&D expenditure. Founded in 1997, Genedata is headquartered in Basel, Switzerland with additional offices in Boston, London, Munich, San Francisco, Singapore, and Tokyo.
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Genedata Imagence 2.0 sets new benchmarks for HCS. It now offers full end-to-end automation, reducing the time spent on image analysis by an order of magnitude