ArticlePLoS computational biology2023
Evaluating the utility of brightfield image data for mechanism of action prediction.
Article in PLoS computational biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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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.
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.
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Who cites it
19 citing papers in PubMed.
- Cell painting and thermal proteome profiling for inference of drug targets and mechanism of action.Molecular systems biology · 2026Article
- Review
- Deep Learning-Powered Scalable Cancer Organ Chip for Cancer Precision Medicine.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Cell-MICS: Detecting Immune Cells With Label-Free Two-Photon Autofluorescence and Deep Learning.Journal of biophotonics · 2026Article
- Deep learning-based in silico labeling for analyzing morphological features of MSCs to predict immunomodulatory capacity.Communications biology · 2026Article
- A self-supervised machine learning pipeline for extracting information from live cell images at multiple doses and timepoints.Scientific reports · 2026Article
- Functional Precision Medicine Using MicroOrganoSpheres for Treatment Response Prediction in Advanced Colorectal Cancer.JCO precision oncology · 2026Article
- Alternate dyes for image-based profiling assays.SLAS discovery : advancing life sciences R & D · 2025Article
- Using Beads as a Focus Fiduciary to Aid Software-Based Autofocus Accuracy in Microscopy.Bio-protocol · 2025Article
- Deep learning identifies heterogeneous subpopulations in breast cancer cell lines.npj biomedical innovations · 2025Article
- Microfluidic technologies for enhancing the potency, predictability and affordability of adoptive cell therapies.Nature biomedical engineering · 2025Review
- Evaluating feature extraction in ovarian cancer cell line co-cultures using deep neural networks.Communications biology · 2025Article
- Cell Painting: a decade of discovery and innovation in cellular imaging.Nature methods · 2025Review
- Integrating Biological Knowledge for Robust Microscopy Image Profiling onProceedings. IEEE International Conference on Computer Vision · 2025Article
- Article
- Cell-vision fusion: A Swin transformer-based approach for predicting kinase inhibitor mechanism of action from Cell Painting data.iScience · 2024Article
- A Decade in a Systematic Review: The Evolution and Impact of Cell Painting.bioRxiv : the preprint server for biology · 2024Article
- Article
- Cell Painting-based bioactivity prediction boosts high-throughput screening hit-rates and compound diversity.Nature communications · 2024Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Fluorescence staining techniques, such as Cell Painting, together with fluorescence microscopy have proven invaluable for visualizing and quantifying the effects that drugs and other perturbations have on cultured cells. However, fluorescence microscopy is expensive, time-consuming, labor-intensive, and the stains applied can be cytotoxic, interfering with the activity under study. The simplest form of microscopy, brightfield microscopy, lacks these downsides, but the images produced have low contrast and the cellular compartments are difficult to discern. Nevertheless, by harnessing deep learning, these brightfield images may still be sufficient for various predictive purposes. In this study, we compared the predictive performance of models trained on fluorescence images to those trained on brightfield images for predicting the mechanism of action (MoA) of different drugs. We also extracted CellProfiler features from the fluorescence images and used them to benchmark the performance. Overall, we found comparable and largely correlated predictive performance for the two imaging modalities. This is promising for future studies of MoAs in time-lapse experiments for which using fluorescence images is problematic. Explorations based on explainable AI techniques also provided valuable insights regarding compounds that were better predicted by one modality over the other.
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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 Socratic graph.