ArticleScientific reports2025
Self-supervision advances morphological profiling by unlocking powerful image representations.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 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
27 citing papers in PubMed.
- Development of a universal imaging "phenome" using shape, appearance and motion (SAM) features and the SAM Phenotype Observation Tool (SPOT).Nature communications · 2026Article
- Integrating chemical structures as treatments improves representations of microscopy images for morphological profiling.PLoS computational biology · 2026Article
- Retrieval-Based Evaluation of Cell Painting Feature Spaces Reveals Differences in the Preservation of Biologically Meaningful Phenotypic Similarity.International journal of molecular sciences · 2026Article
- Deep learning-based prediction of gene expression from histopathology identifies NR5A1 as a candidate biomarker and druggable target in high-grade serous ovarian carcinoma.Journal of ovarian research · 2026Article
- Progress and new challenges in image-based profiling.Molecular systems biology · 2026Review
- Intelligent identification of medical and veterinary intracellular protozoa by using self-supervised learning.Parasites & vectors · 2026Article
- Large scale compound selection guided by cell painting reveals activity cliffs and functional relationships.Communications biology · 2026Article
- A self-supervised machine learning pipeline for extracting information from live cell images at multiple doses and timepoints.Scientific reports · 2026Article
- A pooled Cell Painting CRISPR screening platform enables de novo inference of gene function by self-supervised deep learning.Nature communications · 2025Article
- Cell-DINO: Self-supervised image-based embeddings for cell fluorescent microscopy.PLoS computational biology · 2025Article
- SubCell: Proteome-aware vision foundation models for microscopy capture single-cell biology.bioRxiv : the preprint server for biology · 2025Article
- Article
- Cross modality learning of cell painting and transcriptomics data improves mechanism of action clustering and bioactivity modelling.Scientific reports · 2025Article
- A genome-wide atlas of human cell morphology.Nature methods · 2025Article
- Cell Painting: a decade of discovery and innovation in cellular imaging.Nature methods · 2025Review
- Semisupervised Contrastive Learning for Bioactivity Prediction Using Cell Painting Image Data.Journal of chemical information and modeling · 2025Article
- uniDINO: Assay-independent feature extraction for fluorescence microscopy images.Computational and structural biotechnology journal · 2025Article
- Decoding phenotypic screening: A comparative analysis of image representations.Computational and structural biotechnology journal · 2024Article
- LUMIC: Latent diffUsion for Multiplexed Images of Cells.bioRxiv : the preprint server for biology · 2024Article
- Cell-vision fusion: A Swin transformer-based approach for predicting kinase inhibitor mechanism of action from Cell Painting data.iScience · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cell Painting is an image-based assay that offers valuable insights into drug mechanisms of action and off-target effects. However, traditional feature extraction tools such as CellProfiler are computationally intensive and require frequent parameter adjustments. Inspired by recent advances in AI, we trained self-supervised learning (SSL) models DINO, MAE, and SimCLR on a subset of the JUMP Cell Painting dataset to obtain powerful representations for Cell Painting images. We assessed these SSL features for reproducibility, biological relevance, predictive power, and transferability to novel tasks and datasets. Our best model (DINO) surpassed CellProfiler in drug target and gene family classification, significantly reducing computational time and costs. DINO showed remarkable generalizability without fine-tuning, outperforming CellProfiler on an unseen dataset of genetic perturbations. In bioactivity prediction, DINO achieved comparable performance to models trained directly on Cell Painting images, with only a small gap between supervised and self-supervised approaches. Our study demonstrates the effectiveness of SSL methods for morphological profiling, suggesting promising research directions for improving the analysis of related image modalities.
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.