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Biological design with machine learning and limited data.

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If you have a question about this talk, please contact Fulvio Forni .

AI and machine learning have rapidly emerged as promising tools for cellular engineering and optimisation. Yet the complexities of biological measurements often limit the applicability of state-of-the-art algorithms that require large and well-curated data for training. This gap could potentially leave behind many academic and industry laboratories that could hugely benefit from this technology. In this talk, I will describe recent applications of machine learning for in silico discovery and optimisation, with a focus on small and heterogeneous datasets typically encountered in biological design tasks. Examples include predicting protein expression/function from sequence information, low-N drug discovery against complex diseases, and optimisation of gene circuits for metabolite production.

The seminar will be held in LR3A , Department of Engineering, and online (zoom): https://newnham.zoom.us/j/92544958528?pwd=YS9PcGRnbXBOcStBdStNb3E0SHN1UT09

This talk is part of the CUED Control Group Seminars series.

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