ArticleCell2018
Seeing More: A Future of Augmented Microscopy.
Article in Cell, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Cell context-dependent in silico organelle localization in label-free microscopy images.Nature methods · 2026Article
- CyberSco.Py an open-source software for event-based, conditional microscopy.Scientific reports · 2022Article
- Interpretable deep learning uncovers cellular properties in label-free live cell images that are predictive of highly metastatic melanoma.Cell systems · 2021Article
- The role of convolutional neural networks in scanning probe microscopy: a review.Beilstein journal of nanotechnology · 2021Review
- Community standards for open cell migration data.GigaScience · 2020Article
- Deep learning for cellular image analysis.Nature methods · 2019Review
- High-performance medicine: the convergence of human and artificial intelligence.Nature medicine · 2019Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Microscope images are information rich. In this issue of Cell, Christiansen et al. show that label-free images of cells can be used to predict fluorescent labels representing cell type, state, and organelle distribution using a deep-learning framework. This paves the way for computationally multiplexed assays derived from inexpensive label-free microscopy.
Indexed as
Identifiers
29677507What 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.