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Details of Grant 

EPSRC Reference: EP/S001360/1
Title: CoED: Deep reinforcement learning for improving research productivity in the life science sector.
Principal Investigator: Ezer, Dr D
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Google The Alan Turing Institute
Department: Statistics
Organisation: University of Warwick
Scheme: EPSRC Fellowship - NHFP
Starts: 29 June 2018 Ends: 28 June 2021 Value (£): 329,899
EPSRC Research Topic Classifications:
Bioinformatics
EPSRC Industrial Sector Classifications:
Information Technologies R&D
Related Grants:
Panel History:
Panel DatePanel NameOutcome
08 May 2018 EPSRC UKRI CL Innovation Fellowship Interview Panel 3 - 8 and 9 May 2018 Announced
Summary on Grant Application Form
There have recently been significant leaps in deep reinforcement learning algorithms, with notable successes in games such as Atari arcade games and Go; however, there is still a need to adapt these techniques to be more widely applicable in other domains, such as the life science sector. Identifying regulatory relationships between genes is one of the primary research activities carried out by molecular biologists and geneticists, since learning the structure of gene regulatory networks is critical for many applications, for example understanding the origins of many diseases and how crops respond to their environments. Biologists sequentially conduct experiments that provide information about the gene network structure, but they must operate under strict cost and time limits. This project aims to formulate this experiment design procedure in a reinforcement-learning framework, to ascertain how biologists should prioritise experiments to maximise information about the gene networks, under constraints. The primary deliverable will be a Computer-aided Experimental Design (CoED) software tool to aid researchers in utilising their resources most effectively. This reinforcement-learning framework could also be used to identify the bottlenecks for biomedical research, such as the pricing model or the time-intensity of certain experiments, thereby identifying the most impactful areas for further development in experimental methodology. We will deliver impact by providing consultation services to laboratory supply and service providers, and through our collaboration with our industrial partner Google Brain Genomics. This project primarily aligns with the new approaches to data science and high productivity services through specialised artificial intelligence priority areas of this call.
Key Findings
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Potential use in non-academic contexts
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Summary
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Organisation Website: http://www.warwick.ac.uk