Using deep learning and graph theory to determine biomolecular structures in cryoEM images

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• Dr Beatriz Costa-Gomes, The Alan Turing Institute
• Thursday 27 October 2022, 13:00-14:00
• Teaching Room, James Dyson Building, Dept of Engineering, University of Cambridge.

If you have a question about this talk, please contact James Walsh.

CryoEM is an imaging technique that allows the determination of structures at the atomic level. These highly noisy images have been extensively studied and the detection of single particles (i.e., isotropic molecules in a homogeneous sample) has been automated. However, for more complex structures such as fibrils, or for samples where more than one particle is of interest, the annotations must be manual. In this project, we aim to apply deep learning methods to improve the annotation pipeline by searching for geometric similarities along with image features. Furthermore, these methods are generalisable to other scientific imagery fields: a tool (scivision) is being developed to facilitate and bridge between them.

This talk is part of the Future Infrastructure and Built Environment (FIBE) Lunchtime Seminars series.

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• Future Infrastructure and Built Environment (FIBE) Lunchtime Seminars
• Teaching Room, James Dyson Building, Dept of Engineering, University of Cambridge

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