Pipeline for particle picking in cryo-electron microscopy images using convolutional neural networks trained from positive and unlabeled examples. Also featuring micrograph and tomogram denoising with DNNs.
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Updated
Apr 17, 2024 - Jupyter Notebook
Pipeline for particle picking in cryo-electron microscopy images using convolutional neural networks trained from positive and unlabeled examples. Also featuring micrograph and tomogram denoising with DNNs.
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.
TomoNet is a GUI based pipeline package focusing on cryoET and STA data processing
KLT picker: Particle picking using data-driven optimal templates (Python version)
2D NN-based particle picking from sparse labels
REliable PIcking by Consensus (REPIC) - an ensemble learning methodology for cryo-EM particle picking
Cellular content mining and particle localization
Data analysis scripts, patches, and installation guides for cryo-EM particle pickers.
Open Source python module to enable batch gold particle picking, filtering, processing & statistical modelling of TEM images produced by IGEM (immunogold electron microscopy).
Plot metrics from a Topaz training run
KLT picker: Particle picking using data-driven optimal templates (MATLAB version)
REliable PIcking by Consensus (REPIC)
Web application in Dash and Plotly.js for displaying cryo-EM micrographs and visualizing coordinate overlays.
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