Over 600 senior-level delegates representing internationally renowned research & academic institutions, clinical research institutions and pharmaceutical companies.
Over 20 case studies and presentations demonstrating the latest synthetic biology tools and their therapeutic applications.
Including 2 Interactive Streams:
Co-located with the highly established 11th Annual Next Generation Sequencing & Clinical Diagnostics Congress, 7th Annual Single Cell Analysis Congress, 5th Annual Genome Editing Congress and the Digital PCR Congress.
For more information contact b.copeman@oxfordglobal.co.uk.
Go Social: Join our Genomics Networking Group on LinkedIn, follow us on Twitter @xgenseq & join the conversation at #GENSERIESUK19
2019 Live Webinar
Machine Learning for and by Synthetic Biology
Jean-Loup Faulon, Director, Institute of Systems & Synthetic Biology iSSB and Professor, University of Manchester
Tuesday October 8, 2019 at 9:30am
Register for Free: https://bit.ly/2JvoY8C
Benefits of Attending
Over 20 case studies and presentations demonstrating the latest synthetic biology tools and their therapeutic applications.
Including 2 Interactive Streams:
- Synthetic Biology – Tool Design & Development
- Synthetic Biology – Evaluating Applications
Co-located with the highly established 11th Annual Next Generation Sequencing & Clinical Diagnostics Congress, 7th Annual Single Cell Analysis Congress, 5th Annual Genome Editing Congress and the Digital PCR Congress.
For more information contact b.copeman@oxfordglobal.co.uk.
Go Social: Join our Genomics Networking Group on LinkedIn, follow us on Twitter @xgenseq & join the conversation at #GENSERIESUK19
2019 Live Webinar
Machine Learning for and by Synthetic Biology
Jean-Loup Faulon, Director, Institute of Systems & Synthetic Biology iSSB and Professor, University of Manchester
Tuesday October 8, 2019 at 9:30am
Register for Free: https://bit.ly/2JvoY8C
Benefits of Attending
- Discussion on whether machine learning is needed and doable in Synthetic Biology
- Presentation of a reinforcement learning approach for optimizing cell-free composition maximizing protein production
- Exploration of neural-like computation by a metabolic perceptron to classify biomarker signals
