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Found 5 papers, 2 papers with code
Amortized Inference for Heterogeneous Reconstruction in Cryo-EM
Cryo-electron microscopy (cryo-EM) is an imaging modality that provides unique insights into the dynamics of proteins and other building blocks of life.
Exploring generative atomic models in cryo-EM reconstruction
Although reconstruction algorithms typically model the 3D volume as a generic function parameterized as a voxel array or neural network, the underlying atomic structure of the protein of interest places well-defined physical constraints on the reconstructed structure.
CryoDRGN2: Ab Initio Neural Reconstruction of 3D Protein Structures From Real Cryo-EM Images
In this work we describe cryoDRGN2, an ab initio reconstruction algorithm, which can jointly estimate image poses and learn a neural model of a distribution of 3D structures on real heterogeneous cryo-EM data.
Explicitly disentangling image content from translation and rotation with spatial-VAE
Given an image dataset, we are often interested in finding data generative factors that encode semantic content independently from pose variables such as rotation and translation.
Reconstructing continuous distributions of 3D protein structure from cryo-EM images
Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structure of proteins and other macromolecular complexes at near-atomic resolution.
