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SBI2 High Content Meeting 2021

Virtual Conference
October 4–6, 2021

Join Genedata experts at the virtual SBI2 High Content 2021 conference.

Don't miss your chance to find out more about our new solution, Genedata Imagence®, a high content screening (HCS) image analysis software based on deep learning. The software lets you train a deep neural network to classify cellular phenotypes in HCS images for unbiased, high-quality results. It automates analysis, putting the power of deep learning algorithms in the hands of assay biologists.  To get more information about Genedata Imagence or to arrange a meeting, please contact imagence(at)genedata.com.

Don't miss the opportunity to see how Genedata Screener® for HCS. The world’s top pharma and contract research organizations rely on Screener for HCS for a streamlined high content analysis. Screener manages massive, multi-featured HCS data, and uncovers relevant features with powerful analysis methods. 

If you would like to schedule a meeting in advance or receive more information on Genedata Screener, please contact screener(at)genedata.com.

Recommended Oral Presentations

AI and Automation of High-Content Screening Analysis
Simon Gutbier, Senior Scientist, Roche
James Pilling, Scientist, AstraZeneca
Michelle Newman, Scientific Account Manager, Genedata
Mario Wieser, Scientific Data Analyst, Genedata

Wednesday, October 6 | 10:00–11:00 am

In the drug discovery setting, phenotypic screening relies on more physiologically-relevant, target-unbiased readouts and is thought to generate leads beyond the reach of conventional target-based screening. Specifically, image-based high content screening (HCS) is an increasingly favored approach, thanks to innovations in model systems and assays such as Cell Painting, as well as advances in automation of high-throughput microscopy. Imaging-based HCS requires analysis solutions that can efficiently process high-volume data and derive new insight from complex results. In this roundtable, we will address the use of artificial intelligence (AI) and machine learning-based approaches for HCS analysis, including available enterprise-ready solutions and challenges surrounding the generation of training data. Our participants will include drug discovery as well as software and AI experts, and we hope to compare experiences and speculate about future possibilities.