End-to-end experimental and machine learning workflows for predictive genetic design
High-throughput experiments combined with emerging machine learning (ML) technologies are enabling data-centric biological design workflows for synthetic biology.
In this talk, I will introduce some of the ways my group are contributing to this area, both from an experimental perspective, where nanopore sequencing is being used to characterise diverse libraries of genetic parts, to improved data processing pipelines and the rigorous optimisation of machine learning models for predicting the function of genetic parts from sequence alone. I aim to show the value of considering
these workflows from end-to-end and how this can help improve quality and reproducibility of results.
School of Biological Sciences, University of Bristol, UK:
- Pierre-Aurelien Gilliot
- Matthew J. Tarnowski
- Thomas E. Gorochowski
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End-to-end experimental and machine learning workflows for predictive genetic design
High-throughput experiments combined with emerging machine learning (ML) technologies are enabling data-centric biological design workflows for synthetic biology.
In this talk, Thomas introduced some of the ways his group is contributing to this area, both from an experimental perspective, where nanopore sequencing is being used to characterise diverse libraries of genetic parts, to improved data processing pipelines and the rigorous optimisation of machine learning models for predicting the function of genetic parts from sequence alone. Thomas showed the value of considering these workflows from end-to-end and how this can help improve quality and reproducibility of results.
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