ArticleNature methods2023
Convolutional networks for supervised mining of molecular patterns within cellular context.
Article in Nature methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 76 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.
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Who cites it
76 citing papers in PubMed, 144 citations in OpenAlex.
- MemBrain v2: an end-to-end tool for the analysis of membranes in cryo-electron tomography.Nature methods · 2026Article
- The membrane-to-cortex distance regulates mDia1 activity to control cortical mechanics.Nature communications · 2026Article
- Article
- Graph identification of proteins in tomograms (GRIP-Tomo) 2.0: Topologically aware classification for proteins.Protein science : a publication of the Protein Society · 2026Article
- Lessons learned from a Kaggle challenge for particle picking in cryo-electron tomography.Nature methods · 2026Article
- Article
- Interactive segmentation of membrane and membrane-mimic densities in cryo-EM maps.Acta crystallographica. Section D, Structural biology · 2026Article
- Co-translational protein targeting to mitochondria in the context of co-translational protein maturation.Protein science : a publication of the Protein Society · 2026Review
- Applications and prospects of cryo-electron tomography in drug discovery and understanding disease.Current opinion in structural biology · 2026Review
- Advances in cryo-EM that have shaped mechanistic models of membrane-attack-complex assembly and regulation.IUCrJ · 2026Review
- 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
- Surface Morphometrics reveals local membrane thickness variation in organellar subcompartments.The Journal of cell biology · 2026Article
- Deep-learning methods for contrast enhancement and artifact reduction in cryo-electron tomography: a systematic analysis of the state of the art and proposed improvements.Acta crystallographica. Section D, Structural biology · 2026Article
- TANGO: Analysis and curation of particles in cryo-electron tomography.Nature communications · 2026Article
- Leveraging AI for cell biology discovery.Biochemical Society transactions · 2026Review
- MVGFormer: Multi-view perspective with graph-guided transformer for cryo-ET segmentation.Knowledge-based systems · 2026Article
- Structural Analysis of Membrane Proteins in Cell-Derived Microvesicles.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Advances in Crystallography and Cryo-Electron Microscopy for the Study of Membrane Proteins.Advances in experimental medicine and biology · 2026Review
- There and back again: a cell biologist's journey from organelles to molecules.Biological chemistry · 2025Review
- Mechanisms of COPII coat assembly and cargo recognition in the secretory pathway.Nature reviews. Molecular cell biology · 2025Review
16 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors at 8 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cryo-electron tomograms capture a wealth of structural information on the molecular constituents of cells and tissues. We present DeePiCt (deep picker in context), an open-source deep-learning framework for supervised segmentation and macromolecular complex localization in cryo-electron tomography. To train and benchmark DeePiCt on experimental data, we comprehensively annotated 20 tomograms of Schizosaccharomyces pombe for ribosomes, fatty acid synthases, membranes, nuclear pore complexes, organelles, and cytosol. By comparing DeePiCt to state-of-the-art approaches on this dataset, we show its unique ability to identify low-abundance and low-density complexes. We use DeePiCt to study compositionally distinct subpopulations of cellular ribosomes, with emphasis on their contextual association with mitochondria and the endoplasmic reticulum. Finally, applying pre-trained networks to a HeLa cell tomogram demonstrates that DeePiCt achieves high-quality predictions in unseen datasets from different biological species in a matter of minutes. The comprehensively annotated experimental data and pre-trained networks are provided for immediate use by the community.
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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.