ArticleCytometry. Part A : the journal of the International Society for Analytical Cytology2011
Image-derived, three-dimensional generative models of cellular organization.
Article in Cytometry. Part A : the journal of the International Society for Analytical Cytology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 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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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.
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Who cites it
25 citing papers in PubMed, 59 citations in OpenAlex.
- Learning Morphological, Spatial, and Dynamic Models of Cellular Components.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Cellular harmonics for the morphology-invariant analysis of molecular organization at the cell surface.Nature computational science · 2023Article
- Basal body organization and cell geometry during the cell cycle inMolecular biology of the cell · 2023Article
- A deep generative model of 3D single-cell organization.PLoS computational biology · 2022Article
- Data science in cell imaging.Journal of cell science · 2021Review
- Article
- Image-derived models of cell organization changes during differentiation and drug treatments.Molecular biology of the cell · 2020Article
- Applications and Challenges of Machine Learning to Enable Realistic Cellular Simulations.Frontiers in physics · 2020Article
- CellOrganizer: Learning and Using Cell Geometries for Spatial Cell Simulations.Methods in molecular biology (Clifton, N.J.) · 2019Article
- 3D Shape Modeling for Cell Nuclear Morphological Analysis and Classification.Scientific reports · 2018Article
- Article
- Subcellular protein expression models for microsatellite instability in colorectal adenocarcinoma tissue images.BMC bioinformatics · 2016Article
- 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
- 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
- Survey statistics of automated segmentations applied to optical imaging of mammalian cells.BMC bioinformatics · 2015Article
- Extracting meaning from biological imaging data.Molecular biology of the cell · 2014Article
- Toward the virtual cell: automated approaches to building models of subcellular organization "learned" from microscopy images.BioEssays : news and reviews in molecular, cellular and developmental biology · 2012Review
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
Abstract
Given the importance of subcellular location to protein function, computational simulations of cell behaviors will ultimately require the ability to model the distributions of proteins within organelles and other structures. Toward this end, statistical learning methods have previously been used to build models of sets of two-dimensional microscope images, where each set contains multiple images for a single subcellular location pattern. The model learned from each set of images not only represents the pattern but also captures the variation in that pattern from cell to cell. The models consist of sub-models for nuclear shape, cell shape, organelle size and shape, and organelle distribution relative to nuclear and cell boundaries, and allow synthesis of images with the expectation that they are drawn from the same underlying statistical distribution as the images used to train them. Here we extend this generative models approach to three dimensions using a similar framework, permitting protein subcellular locations to be described more accurately. Models of different patterns can be combined to yield a synthetic multi-channel image containing as many proteins as desired, something that is difficult to obtain by direct microscope imaging for more than a few proteins. In addition, the model parameters represent a more compact and interpretable way of communicating subcellular patterns than descriptive image features and may be particularly effective for automated identification of changes in subcellular organization caused by perturbagens.
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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.