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SUMMARY:Using deep learning and graph theory to determine biomolecular str
 uctures in cryoEM images - Dr Beatriz Costa-Gomes\, The Alan Turing Instit
 ute
DTSTART:20221027T120000Z
DTEND:20221027T130000Z
UID:TALK192644@talks.cam.ac.uk
CONTACT:James Walsh
DESCRIPTION:CryoEM is an imaging technique that allows the determination o
 f structures at the atomic level. These highly noisy images have been exte
 nsively studied and the detection of single particles (i.e.\, isotropic mo
 lecules in a homogeneous sample) has been automated. However\, for more co
 mplex structures such as fibrils\, or for samples where more than one part
 icle 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. Furthermor
 e\, these methods are generalisable to other scientific imagery fields: a 
 tool (scivision) is being developed to facilitate and bridge between them.
LOCATION:Teaching Room\, James Dyson Building\, Dept of Engineering\, Univ
 ersity of Cambridge
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