ReviewAnnual review of biomedical data science2020
Computational Methods for Single-Particle Electron Cryomicroscopy.
Review in Annual review of biomedical data science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
40 citing papers in PubMed.
- EXPECTATION-MAXIMIZATION FOR STRUCTURE DETERMINATION DIRECTLY FROM CRYO-EM MICROGRAPHS.Inverse problems and imaging (Springfield, Mo.) · 2027Article
- The CAHRA Challenge: A Community-Wide Assessment of Cryo-EM Heterogeneous Reconstruction Algorithms.bioRxiv : the preprint server for biology · 2026Article
- Article
- Fast Rigid Alignment of Heterogeneous Images in Sliced Wasserstein Distance.SIAM journal on imaging sciences · 2026Article
- Hierarchical geometric deep learning enables scalable analysis of molecular dynamics.The Journal of chemical physics · 2026Article
- Interactive segmentation of membrane and membrane-mimic densities in cryo-EM maps.Acta crystallographica. Section D, Structural biology · 2026Article
- A Novel Machine-Learning Based Method for Resolving Secondary Structure Topology in Medium-Resolution Cryo-EM Density Maps.International journal of molecular sciences · 2026Article
- A widespread protein misfolding mechanism is differentially rescued in vitro by chaperones based on gene essentiality.Nature communications · 2025Article
- A widespread protein misfolding mechanism is differentially rescued by chaperones based on gene essentiality.bioRxiv : the preprint server for biology · 2025Article
- In situ cryo-electron microscopy and tomography of cellular and organismal samples.Current opinion in structural biology · 2025Review
- Efficient high-resolution refinement in cryo-EM with stochastic gradient descent.Acta crystallographica. Section D, Structural biology · 2025Article
- Article
- A large-scale curated and filterable dataset for cryo-EM foundation model pre-training.Scientific data · 2025Article
- InstaMap: instant-NGP for cryo-EM density maps.Acta crystallographica. Section D, Structural biology · 2025Article
- TheApplied and computational harmonic analysis · 2024Article
- MAXIMUM LIKELIHOOD FOR HIGH-NOISE GROUP ORBIT ESTIMATION AND SINGLE-PARTICLE CRYO-EM.Annals of statistics · 2024Article
- A robust normalized local filter to estimate compositional heterogeneity directly from cryo-EM maps.Nature communications · 2023Article
- Novel Artificial Intelligence-Based Approaches for Ab Initio Structure Determination and Atomic Model Building for Cryo-Electron Microscopy.Micromachines · 2023Review
- A large expert-curated cryo-EM image dataset for machine learning protein particle picking.Scientific data · 2023Article
- An Unsupervised Classification Algorithm for Heterogeneous Cryo-EM Projection Images Based on Autoencoders.International journal of molecular sciences · 2023Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Single-particle electron cryomicroscopy (cryo-EM) is an increasingly popular technique for elucidating the three-dimensional structure of proteins and other biologically significant complexes at near-atomic resolution. It is an imaging method that does not require crystallization and can capture molecules in their native states. In single-particle cryo-EM, the three-dimensional molecular structure needs to be determined from many noisy two-dimensional tomographic projections of individual molecules, whose orientations and positions are unknown. The high level of noise and the unknown pose parameters are two key elements that make reconstruction a challenging computational problem. Even more challenging is the inference of structural variability and flexible motions when the individual molecules being imaged are in different conformational states. This review discusses computational methods for structure determination by single-particle cryo-EM and their guiding principles from statistical inference, machine learning, and signal processing that also play a significant role in many other data science applications.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.