Cryogenic Electron Microscopy (cryo-EM) enables structural biologists to capture high-resolution images, offering a close-up view of biological processes at the near-atomic level. Despite its breakthroughs, challenges such as a limited viewing angle, radiation damage, and shot noise complicate the identification of microscopic particles within these images. To tackle these obstacles, the Cryo-EM Technology Platform at the Research of Molecular Pathology (IMP) in collaboration with Pavol Harar at the University of Vienna, developed ‘FakET’. This new data-driven method efficiently generates ‘fake’ electron microscopy images to train Artificial Intelligence analysis software, minimising labour-intensive workflows in particle identification.
Their findings are now published in the journal Structure.
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FakET: new method advances electron microscopy with AI simulations
