There are 10 repositories under cryo-em topic.
A collaborative list of awesome CryoEM (Cryo Electron Microscopy) resources.
A curated list of awesome computational cryo-EM methods.
Self-supervised learning for isotropic cryoET reconstruction
pytorch implementation of noise2noise for Cryo-EM image denoising
register 3D point clouds using rotation, translation, and scale transformations.
ArtiaX is an open-source extension of the molecular visualisation program ChimeraX.
Python library that enables scripting access to CryoSPARC, a cryo-EM software package.
cryo-ET particle picking using triplet networks and metric learning
TomoBEAR is a configurable and customizable modular pipeline for streamlined processing of cryo-electron tomographic data for subtomogram averaging.
Conventions for 3DEM software packages
Rigid body fitting of atomic strucures in cryo-electron microscopy density maps
Official Pytorch Implementation of Residual Multiplicative Filter Networks
[BS]物联网工程,[MS]计算机技术,python,mooc资源,机器学习,深度学习,cryo-em[冷冻电子显微镜],3D reconstruction[三维重建],Computational Vison。
ManifoldEM Python suite
Emap2sec+: Detecting Protein and DNA/RNA Structures in Cryo-EM Maps of Intermediate Resolution Using Deep Learning
Learning to recover orientations from projections in single-particle cryo-EM
Covariance Estimation and Denoising for Cryo-EM Images (Covariance Wiener Filtering)
Scripts and sources of the block-based reconstruction/refinement in cryo-EM
DiffModeler: a diffusion model based protein complex structure modeling tool.
CryoSPARC is the state-of-the-art platform used globally for obtaining 3D structural information from single particle cryo-EM data. The cryoSPARC platform enables automated, high quality and high-throughput structure discovery of proteins, viruses and molecular complexes for research and drug discovery.
example set up for Relion on AWS ParallelCluster for CryoEM
Simulation of cryo-EM ensemble data from atomic models of molecules exhibiting continuous motions
Overcoming the preferred orientation problem in cryoEM with self-supervised deep-learning