ArticleActa crystallographica. Section D, Structural biology2025
How many (distinguishable) classes can we identify in single-particle analysis?
Article in Acta crystallographica. Section D, Structural biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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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.
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Who cites it
2 citing papers in PubMed.
- Article
- ANTIDOTE: A Metadata-Driven Neural Network for Improving CryoEM 3-D Particle Sorting.bioRxiv : the preprint server for biology · 2025Article
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Authors and funding
13 authors.
Funding
Abstract
Heterogeneity in cryoEM is essential for capturing the structural variability of macromolecules, reflecting their functional states and biological significance. However, estimating heterogeneity remains challenging due to particle misclassification and algorithmic biases, which can lead to reconstructions that blend distinct conformations or fail to resolve subtle differences. Furthermore, the low signal-to-noise ratio inherent in cryo-EM data makes it nearly impossible to detect minute structural changes, as noise often obscures subtle variations in macromolecular projections. In this paper, we investigate the use of p-values associated with the null hypothesis that the observed classification differs from a random partition of the input data set, thereby providing a statistical framework for determining the number of distinguishable classes present in a given data set.
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