ReviewCritical reviews in biochemistry and molecular biology2016
Visualizing quantitative microscopy data: History and challenges.
Review in Critical reviews in biochemistry and molecular biology, 2016. 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.
- Stain-free artificial intelligence-assisted light microscopy for the identification of leukocyte morphology change in presence of bacteria.Frontiers in bioinformatics · 2025Article
- Studentsourcing-Aggregating and reusing data from a practical cell biology course.PLoS computational biology · 2024Article
- IMAGE-IN: Interactive web-based multidimensional 3D visualizer for multi-modal microscopy images.PloS one · 2022Article
- DeepScratch: Single-cell based topological metrics of scratch wound assays.Computational and structural biotechnology journal · 2020Article
- A bird's-eye view of deep learning in bioimage analysis.Computational and structural biotechnology journal · 2020Review
- Data-analysis strategies for image-based cell profiling.Nature methods · 2017Article
- RNAi screens for Rho GTPase regulators of cell shape and YAP/TAZ localisation in triple negative breast cancer.Scientific data · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Data visualization is a fundamental aspect of science. In the context of microscopy-based studies, visualization typically involves presentation of the images themselves. However, data visualization is challenging when microscopy experiments entail imaging of millions of cells, and complex cellular phenotypes are quantified in a high-content manner. Most well-established visualization tools are inappropriate for displaying high-content data, which has driven the development of new visualization methodology. In this review, we discuss how data has been visualized in both classical and high-content microscopy studies; as well as the advantages, and disadvantages, of different visualization methods.
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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.