ArticleSIAM journal on imaging sciences2018
Structural Variability from Noisy Tomographic Projections.
Article in SIAM journal on imaging sciences, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
18 citing papers in PubMed.
- How many (distinguishable) classes can we identify in single-particle analysis?Acta crystallographica. Section D, Structural biology · 2025Article
- Efficient high-resolution refinement in cryo-EM with stochastic gradient descent.Acta crystallographica. Section D, Structural biology · 2025Article
- Article
- Cryo-EM heterogeneity analysis using regularized covariance estimation and kernel regression.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Cryo-EM Heterogeneity Analysis using Regularized Covariance Estimation and Kernel Regression.bioRxiv : the preprint server for biology · 2024Article
- MDTOMO method for continuous conformational variability analysis in cryo electron subtomograms based on molecular dynamics simulations.Scientific reports · 2023Article
- 3DFlex: determining structure and motion of flexible proteins from cryo-EM.Nature methods · 2023Article
- Methods for Cryo-EM Single Particle Reconstruction of Macromolecules Having Continuous Heterogeneity.Journal of molecular biology · 2023Review
- A Bayesian approach to extracting free-energy profiles from cryo-electron microscopy experiments.Scientific reports · 2021Article
- Principal component analysis is limited to low-resolution analysis in cryoEM.Acta crystallographica. Section D, Structural biology · 2021Article
- Cryo-EM Analyses Permit Visualization of Structural Polymorphism of Biological Macromolecules.Frontiers in bioinformatics · 2021Review
- HEMNMA-3D: Cryo Electron Tomography Method Based on Normal Mode Analysis to Study Continuous Conformational Variability of Macromolecular Complexes.Frontiers in molecular biosciences · 2021Article
- Computational Methods for Single-Particle Electron Cryomicroscopy.Annual review of biomedical data science · 2020Review
- Method of moments for 3D single particleInverse problems · 2020Article
- Hyper-Molecules: on the Representation and Recovery of Dynamical Structures for Applications in Flexible Macro-Molecules in Cryo-EM.Inverse problems · 2020Article
- Cryo-EM reconstruction of continuous heterogeneity by Laplacian spectral volumes.Inverse problems · 2020Article
- Hybrid Electron Microscopy Normal Mode Analysis with Scipion.Protein science : a publication of the Protein Society · 2020Article
- Survey of the analysis of continuous conformational variability of biological macromolecules by electron microscopy.Acta crystallographica. Section F, Structural biology communications · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
In cryo-electron microscopy, the three-dimensional (3D) electric potentials of an ensemble of molecules are projected along arbitrary viewing directions to yield noisy two-dimensional images. The volume maps representing these potentials typically exhibit a great deal of structural variability, which is described by their 3D covariance matrix. Typically, this covariance matrix is approximately low rank and can be used to cluster the volumes or estimate the intrinsic geometry of the conformation space. We formulate the estimation of this covariance matrix as a linear inverse problem, yielding a consistent least-squares estimator. For
Indexed as
Identifiers
What OpenQuestion holds
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.