ArticleMethods in cell biology2012
CellOrganizer: Image-derived models of subcellular organization and protein distribution.
Article in Methods in cell biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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
18 citing papers in PubMed.
- A scoping study of the whole-cell imaging literature as a foundation for the emerging field of cell anatomy.BMC biology · 2026Article
- Automated segmentation of soft X-ray tomography: Native cellular structure with submicron resolution at high-throughput for whole-cell quantitative imaging in yeast.Molecular biology of the cell · 2025Article
- A scoping study of the whole-cell imaging literature: a foundational corpus, potential for mesoscale data synthesis, and implications for standardization of an emerging field.bioRxiv : the preprint server for biology · 2025Article
- Basal body organization and cell geometry during the cell cycle inMolecular biology of the cell · 2023Article
- PD-1 suppresses the maintenance of cell couples between cytotoxic T cells and target tumor cells within the tumor.Science signaling · 2020Article
- An Open-Source Mesh Generation Platform for Biophysical Modeling Using Realistic Cellular Geometries.Biophysical journal · 2020Article
- CACTUS: cancer image annotating, calibrating, testing, understanding and sharing in breast cancer histopathology.BMC research notes · 2020Article
- Evaluation of methods for generative modeling of cell and nuclear shape.Bioinformatics (Oxford, England) · 2019Article
- Cell Membrane Tracking in Living Brain Tissue Using Differential Interference Contrast Microscopy.IEEE transactions on image processing : a publication of the IEEE Signal Processing Society · 2018Article
- Review
- Point process models for localization and interdependence of punctate cellular structures.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2016Article
- Building cell models and simulations from microscope images.Methods (San Diego, Calif.) · 2016Review
- Unbiased Rare Event Sampling in Spatial Stochastic Systems Biology Models Using a Weighted Ensemble of Trajectories.PLoS computational biology · 2016Article
- Visualizing quantitative microscopy data: History and challenges.Critical reviews in biochemistry and molecular biology · 2016Review
- Automated Learning of Subcellular Variation among Punctate Protein Patterns and a Generative Model of Their Relation to Microtubules.PLoS computational biology · 2015Article
- Joint modeling of cell and nuclear shape variation.Molecular biology of the cell · 2015Article
- Modeling for (physical) biologists: an introduction to the rule-based approach.Physical biology · 2015Review
- Extensible visualization and analysis for multidimensional images using Vaa3D.Nature protocols · 2014Article
Corrections and comments
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Authors and funding
1 author.
Funding
Abstract
This chapter describes approaches for learning models of subcellular organization from images. The primary utility of these models is expected to be from incorporation into complex simulations of cell behaviors. Most current cell simulations do not consider spatial organization of proteins at all, or treat each organelle type as a single, idealized compartment. The ability to build generative models for all proteins in a proteome and use them for spatially accurate simulations is expected to improve the accuracy of models of cell behaviors. A second use, of potentially equal importance, is expected to be in testing and comparing software for analyzing cell images. The complexity and sophistication of algorithms used in cell-image-based screens and assays (variously referred to as high-content screening, high-content analysis, or high-throughput microscopy) is continuously increasing, and generative models can be used to produce images for testing these algorithms in which the expected answer is known.
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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.