Self-supervised learning for isotropic cryoET reconstruction
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Updated
Jul 11, 2024 - Python
Self-supervised learning for isotropic cryoET reconstruction
A curated list of awesome computational cryo-ET methods.
ArtiaX is an open-source extension of the molecular visualisation program ChimeraX.
Self-supervised deep learning for denoising and missing wedge reconstruction of cryo-ET tomograms
cryo-ET particle picking by representation and metric learning
Pipeline for the automatic detection and segmentation of particles and cellular structures in 3D Cryo-ET data, based on deep learning (convolutional neural networks).
TomoBEAR is a configurable and customizable modular pipeline for streamlined processing of cryo-electron tomographic data for subtomogram averaging.
TomoNet is a GUI based pipeline package focusing on cryoET and STA data processing
structural heterogeneity analysis for cryo-ET subtomogram
Cellular content mining and particle localization
Toolbox for post-correlation cryo-CLEM workflow developed at Chlanda Lab, Heidelberg University.
PyTorch implementation of "Open-set Recognition of Unseen Macromolecules in Cellular Electron Cryo-Tomograms by Soft Large Margin Centralized Cosine Loss"
Denoising and segmentation networks for cryoET based on U-net architecture implemented in Pytorch
A napari plugin for the DeepFinder library which includes display, annotation, target generation, segmentation and clustering functionalities. An orthoslice view has been added for an easier visualisation and annotation process.
Python scritps for rendering and distance analysis of proteins (proteasome) and segmentations (poly-GA aggregates) in Cryo-ET
A tool to normalize CryoET data by matching amplitude spectrums.
2D NN-based particle picking from sparse labels
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