ArticleIEEE signal processing magazine2020
Single-particle cryo-electron microscopy: Mathematical theory, computational challenges, and opportunities.
Article in IEEE signal processing magazine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
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Who cites it
45 citing papers in PubMed, 140 citations in OpenAlex.
- EXPECTATION-MAXIMIZATION FOR STRUCTURE DETERMINATION DIRECTLY FROM CRYO-EM MICROGRAPHS.Inverse problems and imaging (Springfield, Mo.) · 2027Article
- prismPYP: Power-spectrum and image domain learning for self-supervised micrograph evaluation.Structure (London, England : 1993) · 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
- CryoFSL: an annotation-efficient, few-shot learning framework for robust protein particle picking in cryo-electron microscopy micrographs.Briefings in bioinformatics · 2026Article
- Bayesian perspective for orientation determination in cryo-EM with application to structural heterogeneity analysis.Acta crystallographica. Section D, Structural biology · 2026Article
- Subspace Method of Moments forSIAM journal on imaging sciences · 2026Article
- Bayesian Perspective for Orientation Determination in Cryo-EM with Application to Structural Heterogeneity Analysis.bioRxiv : the preprint server for biology · 2025Article
- CryoFSL: An Annotation-Efficient, Few-Shot Learning Framework for Robust Protein Particle Picking in Cryo-EM Micrographs.bioRxiv : the preprint server for biology · 2025Article
- Self-supervised learning for generalizable particle picking in cryo-EM micrographs.Cell reports methods · 2025Article
- Efficient high-resolution refinement in cryo-EM with stochastic gradient descent.Acta crystallographica. Section D, Structural biology · 2025Article
- Article
- Non-uniform Fourier transform based image classification in single-particle Cryo-EM.Journal of structural biology: X · 2025Article
- Discovering fully semantic representations via centroid- and orientation-aware feature learning.Nature machine intelligence · 2025Article
- Accurate size-based protein localization from cryo-ET tomograms.Journal of structural biology: X · 2024Article
- UPicker: a semi-supervised particle picking transformer method for cryo-EM micrographs.Briefings in bioinformatics · 2024Article
- A deep learning method for simultaneous denoising and missing wedge reconstruction in cryogenic electron tomography.Nature communications · 2024Article
- MAXIMUM LIKELIHOOD FOR HIGH-NOISE GROUP ORBIT ESTIMATION AND SINGLE-PARTICLE CRYO-EM.Annals of statistics · 2024Article
- Self Fourier shell correlation: properties and application to cryo-ET.Communications biology · 2024Article
- Joint micrograph denoising and protein localization in cryo-electron microscopy.Biological imaging · 2024Article
- The advent of preventive high-resolution structural histopathology by artificial-intelligence-powered cryogenic electron tomography.Frontiers in molecular biosciences · 2024Review
Corrections and comments
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Authors and funding
3 authors at 3 institutions in 2 countries.
Funding
Abstract
In recent years, an abundance of new molecular structures have been elucidated using cryo-electron microscopy (cryo-EM), largely due to advances in hardware technology and data processing techniques. Owing to these new exciting developments, cryo-EM was selected by Nature Methods as Method of the Year 2015, and the Nobel Prize in Chemistry 2017 was awarded to three pioneers in the field. The main goal of this article is to introduce the challenging and exciting computational tasks involved in reconstructing 3-D molecular structures by cryo-EM. Determining molecular structures requires a wide range of computational tools in a variety of fields, including signal processing, estimation and detection theory, high-dimensional statistics, convex and non-convex optimization, spectral algorithms, dimensionality reduction, and machine learning. The tools from these fields must be adapted to work under exceptionally challenging conditions, including extreme noise levels, the presence of missing data, and massively large datasets as large as several Terabytes. In addition, we present two statistical models: multi-reference alignment and multi-target detection, that abstract away much of the intricacies of cryo-EM, while retaining some of its essential features. Based on these abstractions, we discuss some recent intriguing results in the mathematical theory of cryo-EM, and delineate relations with group theory, invariant theory, and information theory.
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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.