Topic: particle-picking Goto Github
Some thing interesting about particle-picking
Some thing interesting about particle-picking
particle-picking,REliable PIcking by Consensus (REPIC) - an ensemble learning methodology for cryo-EM particle picking
User: ccameron
Home Page: https://github.com/ccameron/REPIC
particle-picking,Plot metrics from a Topaz training run
User: guillawme
Home Page: https://pypi.org/project/topaztrainmetrics
particle-picking,Open Source python module to enable batch gold particle picking, filtering, processing & statistical modelling of TEM images produced by IGEM (immunogold electron microscopy).
User: jmarsil
Home Page: https://pypi.org/project/pygempick/
particle-picking,TomoBEAR is a configurable and customizable modular pipeline for streamlined processing of cryo-electron tomographic data for subtomogram averaging.
Organization: kudryashevlab
Home Page: https://github.com/KudryashevLab/TomoBEAR/wiki
particle-picking,TomoNet is a GUI based pipeline package focusing on cryoET and STA data processing
User: logicvay2010
Home Page: http://dx.doi.org/10.1017/S2633903X24000060
particle-picking,cryo-ET particle picking by representation and metric learning
Organization: mpi-dortmund
particle-picking,Cellular content mining and particle localization
Organization: nextpyp
Home Page: https://nextpyp.app/milopyp/
particle-picking,2D NN-based particle picking from sparse labels
Organization: nextpyp
Home Page: https://nextpyp.app/
particle-picking,Data analysis scripts, patches, and installation guides for cryo-EM particle pickers.
User: sebseager
particle-picking,Web application in Dash and Plotly.js for displaying cryo-EM micrographs and visualizing coordinate overlays.
User: sebseager
particle-picking,KLT picker: Particle picking using data-driven optimal templates (Python version)
Organization: shkolniskylab
particle-picking,KLT picker: Particle picking using data-driven optimal templates (MATLAB version)
Organization: shkolniskylab
particle-picking,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.
User: tbepler
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