ReviewBioEssays : news and reviews in molecular, cellular and developmental biology2012
Toward the virtual cell: automated approaches to building models of subcellular organization "learned" from microscopy images.
Review in BioEssays : news and reviews in molecular, cellular and developmental biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- PD-1 suppresses the maintenance of cell couples between cytotoxic T cells and target tumor cells within the tumor.Science signaling · 2020Article
- Using simulated fluorescence cell micrographs for the evaluation of cell image segmentation algorithms.BMC bioinformatics · 2017Article
- Imagining the future of bioimage analysis.Nature biotechnology · 2016Article
- Metadata management for high content screening in OMERO.Methods (San Diego, Calif.) · 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
- Quantifying Modes of 3D Cell Migration.Trends in cell biology · 2015Review
- Joint modeling of cell and nuclear shape variation.Molecular biology of the cell · 2015Article
- Highly multiplexed imaging of single cells using a high-throughput cyclic immunofluorescence method.Nature communications · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
We review state-of-the-art computational methods for constructing, from image data, generative statistical models of cellular and nuclear shapes and the arrangement of subcellular structures and proteins within them. These automated approaches allow consistent analysis of images of cells for the purposes of learning the range of possible phenotypes, discriminating between them, and informing further investigation. Such models can also provide realistic geometry and initial protein locations to simulations in order to better understand cellular and subcellular processes. To determine the structures of cellular components and how proteins and other molecules are distributed among them, the generative modeling approach described here can be coupled with high throughput imaging technology to infer and represent subcellular organization from data with few a priori assumptions. We also discuss potential improvements to these methods and future directions for research.
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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.