ArticleNature methods2024
MiLoPYP: self-supervised molecular pattern mining and particle localization in situ.
Article in Nature methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed.
- MemBrain v2: an end-to-end tool for the analysis of membranes in cryo-electron tomography.Nature methods · 2026Article
- Lessons learned from a Kaggle challenge for particle picking in cryo-electron tomography.Nature methods · 2026Article
- Integrating in situ single-particle cryo-electron microscopy with cryo-electron tomography for high-resolution structural biology in native cellular contexts.Current opinion in structural biology · 2026Review
- Semi-supervised clustering with knowledge-guided representation learning in cryo-electron tomography.PLOS digital health · 2026Article
- copick: An open dataset interface and toolkit for collaborative annotation and analysis of cryo-electron tomography data.Protein science : a publication of the Protein Society · 2026Article
- In situ structure determination of conformationally flexible targets using nextPYP.Nature protocols · 2026Review
- PickET: An unsupervised method for localizing macromolecules in cryo-electron tomograms.Research square · 2025Article
- PickET: An unsupervised method for localizing macromolecules in cryo-electron tomograms.bioRxiv : the preprint server for biology · 2025Article
- In situ cryo-ET visualization of mitochondrial depolarization and mitophagic engulfment.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- A hybrid YOLO-UNet3D framework for automated protein particle annotation in Cryo-ET images.Scientific reports · 2025Article
- Article
- TomoCPT: a generalizable model for 3D particle detection and localization in cryo-electron tomograms.Acta crystallographica. Section D, Structural biology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Cryo-electron tomography allows the routine visualization of cellular landscapes in three dimensions at nanometer-range resolutions. When combined with single-particle tomography, it is possible to obtain near-atomic resolution structures of frequently occurring macromolecules within their native environment. Two outstanding challenges associated with cryo-electron tomography/single-particle tomography are the automatic identification and localization of proteins, tasks that are hindered by the molecular crowding inside cells, imaging distortions characteristic of cryo-electron tomography tomograms and the sheer size of tomographic datasets. Current methods suffer from low accuracy, demand extensive and time-consuming manual labeling or are limited to the detection of specific types of proteins. Here, we present MiLoPYP, a two-step dataset-specific contrastive learning-based framework that enables fast molecular pattern mining followed by accurate protein localization. MiLoPYP's ability to effectively detect and localize a wide range of targets including globular and tubular complexes as well as large membrane proteins, will contribute to streamline and broaden the applicability of high-resolution workflows for in situ structure determination.
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.