Reconstruction-Aware Cryo-EM Particle Picking
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The article presents a systematic meta-algorithm for spawning and aggregating multi-class cryo-EM reconstruction jobs, formalizing iterative classification and filtering strategies used by practitioners. It claims to be the first method capable of ab initio reconstruction on datasets with dozens of distinct species, achieving 97% accuracy on a 45-class subset of Tomotwin-100 and 75% on the full dataset, and successfully recovering ribosomal assembly states from an unfiltered experimental cryo-EM dataset. The approach scales with compute resources and aims to underpin automated cryo-EM workflows in contemporary experimental settings.
We describe a systematic approach for spawning and aggregating multi-class cryo-EM reconstruction jobs. This approach formalizes standard ad hoc strategies of iterative classification and filtering ty...
arXiv:2610.01358v1 Announce Type: cross Abstract: Single-particle cryo-electron microscopy (cryo-EM) has become a widely adopted technique for biomolecular structure determination. The conventional c...
arXiv:2606. 31332v1 Announce Type: new Abstract: Protein automodeling from cryo-EM density maps faces unique challenges in enforcing physicochemical validity and managing conformational heterogeneity.
arXiv:2606. 10255v1 Announce Type: cross Abstract: Cryo-electron tomography (cryoET) has emerged as a powerful tool in structural and cellular biology by enabling direct visualization of macromolecular structures within intact cells, thereby linking molecular architecture to cellular organization in a native context.
arXiv:2609.14097v1 Announce Type: cross Abstract: Subtomogram classification in cryo-electron tomography (cryo-ET) is a challenging problem due to the scarcity of labeled examples. While cryo-ET simu...