ArticleStructure (London, England : 1993)2019
De Novo Structural Pattern Mining in Cellular Electron Cryotomograms.
Article in Structure (London, England : 1993), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed, 58 citations in OpenAlex.
- Semi-supervised clustering with knowledge-guided representation learning in cryo-electron tomography.PLOS digital health · 2026Article
- AITom: AI-guided cryo-electron tomography image analyses toolkit.Journal of structural biology · 2025Article
- Frontiers in integrative structural modeling of macromolecular assemblies.QRB discovery · 2025Review
- High-confidence 3D template matching for cryo-electron tomography.Nature communications · 2024Article
- Charting the molecular landscape of the cell.Structure (London, England : 1993) · 2023Review
- Computational Methods Toward Unbiased Pattern Mining and Structure Determination in Cryo-Electron Tomography Data.Journal of molecular biology · 2023Review
- High-throughput cryo-ET structural pattern mining by unsupervised deep iterative subtomogram clustering.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Cryo-electron tomography on focused ion beam lamellae transforms structural cell biology.Nature methods · 2023Review
- Advances in cellular and molecular predatory biology ofFrontiers in microbiology · 2023Review
- Isotropic reconstruction for electron tomography with deep learning.Nature communications · 2022Article
- Better, Faster, Cheaper: Recent Advances in Cryo-Electron Microscopy.Annual review of biochemistry · 2022Review
- Harmony: A Generic Unsupervised Approach for Disentangling Semantic Content from Parameterized Transformations.Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 2022Article
- Review
- Quantitative Cryo-Electron Tomography.Frontiers in molecular biosciences · 2022Review
- FSCC: Few-Shot Learning for Macromolecule Classification Based on Contrastive Learning and Distribution Calibration in Cryo-Electron Tomography.Frontiers in molecular biosciences · 2022Article
- The Human Proteoform Project: Defining the human proteome.Science advances · 2021Article
- UNSUPERVISED DOMAIN ALIGNMENT BASED OPEN SET STRUCTURAL RECOGNITION OF MACROMOLECULES CAPTURED BY CRYO-ELECTRON TOMOGRAPHY.Proceedings. International Conference on Image Processing · 2021Article
- Coming of Age: Cryo-Electron Tomography as a Versatile Tool to Generate High-Resolution Structures at Cellular/Biological Interfaces.International journal of molecular sciences · 2021Review
- Assessment of scoring functions to rank the quality of 3D subtomogram clusters from cryo-electron tomography.Journal of structural biology · 2021Article
- Detecting Protein Communities in Native Cell Extracts by Machine Learning: A Structural Biologist's Perspective.Frontiers in molecular biosciences · 2021Article
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Authors and funding
7 authors at 7 institutions in 2 countries.
Funding
Abstract
Electron cryotomography enables 3D visualization of cells in a near-native state at molecular resolution. The produced cellular tomograms contain detailed information about a plethora of macromolecular complexes, their structures, abundances, and specific spatial locations in the cell. However, extracting this information in a systematic way is very challenging, and current methods usually rely on individual templates of known structures. Here, we propose a framework called "Multi-Pattern Pursuit" for de novo discovery of different complexes from highly heterogeneous sets of particles extracted from entire cellular tomograms without using information of known structures. These initially detected structures can then serve as input for more targeted refinement efforts. Our tests on simulated and experimental tomograms show that our automated method is a promising tool for supporting large-scale template-free visual proteomics analysis.
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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.