A Bayesian View on Cryo-EM Structure Determination
MRC Laboratory of Molecular Biology
Abstract
Three-dimensional (3D) structure determination by single-particle analysis of cryo-electron microscopy (cryo-EM) images requires many parameters to be determined from extremely noisy data. This makes the method prone to overfitting, that is, when structures describe noise rather than signal, in particular near their resolution limit where noise levels are highest. Cryo-EM structures are typically filtered using ad hoc procedures to prevent overfitting, but the tuning of arbitrary parameters may lead to subjectivity in the results. I describe a Bayesian interpretation of cryo-EM structure determination, where smoothness in the reconstructed density is imposed through a Gaussian prior in the Fourier domain. The…
Citation impact
- FWCI
- 25.84
- Percentile
- 100%
- References
- 47
Authors
1Topics & keywords
- Overfitting
- Bayesian probability
- Computer science
- Artificial intelligence
- Algorithm
- Smoothness
- Noise (video)
- Pattern recognition (psychology)