IsoNet-cryoET/spIsoNet
Overcoming the preferred orientation problem in cryoEM with self-supervised deep-learning
When analyzing molecular structures using cryo-electron microscopy (cryoEM), you sometimes encounter 'preferred orientation,' where samples tend to lie in only a few ways, leading to missing information in your 3D reconstruction. spIsoNet helps correct this by using existing data from well-oriented particles to fill in the gaps, giving you a more complete and accurate 3D image of your molecule. This is designed for structural biologists and researchers performing single particle analysis or subtomogram averaging.
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Use this if you are a structural biologist struggling with incomplete 3D reconstructions in cryoEM due to your particles having preferred orientations.
Not ideal if you are working with cryo-electron tomography (cryoET) and need to correct for a missing wedge, as a separate tool called IsoNet is designed for that.
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Language
Python
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MIT
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Last pushed
Aug 13, 2025
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