The project offers an ideal opportunity for graduates with an interest in the application of AI in biology and medicine to gain experience in development of open-source platforms and to work with industrial partners to apply them to real-world challenges.
It is based on Imaging flow cytometry, an analytical technique which delivers images of millions of cells, within a few minutes of measurement. As it is able to both mass-image large populations and resolve sub-cellular image detail, it is a transformative technology for the diagnosis and monitoring of a host of diseases. This project will use the latest algorithms in AI to analyse the large datasets generated using this system and will involve collaboration with a number of clinical and industrial collaborators, and in particular Glaxo SmithKline’s pharmaceutical assessment teams.
Specifically, the project will build upon, Deepometry, an open-source workflow developed with the objective of applying deep learning algorithms to the analysis of cytometry data. This has been developed in collaboration with GSK and the Broad Institute of MIT and Harvard. The project will involve software development and the application of this deep learning tool to a number of exciting datasets. In particular, the development of ‘Deepometry 2’ will extend our current software for use on both 2D and 3D images and enable use the imaging technology to image tissue from patient samples.
The project will run in conjunction with researchers at GSK, Philadelphia, US and Stevenage, UK who will provide financial assistance for the applicant to spend substantial periods of time at GSK and work on key research problems.