ReviewMethods (San Diego, Calif.)2016
Building cell models and simulations from microscope images.
Review in Methods (San Diego, Calif.), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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
15 citing papers in PubMed.
- Generative machine learning unlocks the first proteome-wide image of human cells.bioRxiv : the preprint server for biology · 2026Article
- Learning Morphological, Spatial, and Dynamic Models of Cellular Components.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Basal body organization and cell geometry during the cell cycle inMolecular biology of the cell · 2023Article
- Article
- An assessment tool for computer-assisted semen analysis (CASA) algorithms.Scientific reports · 2022Article
- Multimodal Imaging Mass Spectrometry: Next Generation Molecular Mapping in Biology and Medicine.Journal of the American Society for Mass Spectrometry · 2020Review
- Image-derived models of cell organization changes during differentiation and drug treatments.Molecular biology of the cell · 2020Article
- Learning complex subcellular distribution patterns of proteins via analysis of immunohistochemistry images.Bioinformatics (Oxford, England) · 2020Article
- Applications and Challenges of Machine Learning to Enable Realistic Cellular Simulations.Frontiers in physics · 2020Article
- CytoPacq: a web-interface for simulating multi-dimensional cell imaging.Bioinformatics (Oxford, England) · 2019Article
- Learning unsupervised feature representations for single cell microscopy images with paired cell inpainting.PLoS computational biology · 2019Article
- From observing to predicting single-cell structure and function with high-throughput/high-content microscopy.Essays in biochemistry · 2019Review
- Image-derived generative modeling of pseudo-macromolecular structures - towards the statistical assessment of Electron CryoTomography template matching.BMVC : proceedings of the British Machine Vision Conference. British Machine Vision Conference · 2018Article
- Review
- High-throughput Imaging as a versatile and unbiased discovery tool.Methods (San Diego, Calif.) · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
Abstract
The use of fluorescence microscopy has undergone a major revolution over the past twenty years, both with the development of dramatic new technologies and with the widespread adoption of image analysis and machine learning methods. Many open source software tools provide the ability to use these methods in a wide range of studies, and many molecular and cellular phenotypes can now be automatically distinguished. This article presents the next major challenge in microscopy automation, the creation of accurate models of cell organization directly from images, and reviews the progress that has been made towards this challenge.
Indexed as
Identifiers
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.