ArticleNature communications2025
PhenoProfiler: advancing phenotypic learning for image-based drug discovery.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- From descriptive to generative: foundation-model approaches for spatial transcriptomics.Briefings in bioinformatics · 2026Review
- Retrieval-Based Evaluation of Cell Painting Feature Spaces Reveals Differences in the Preservation of Biologically Meaningful Phenotypic Similarity.International journal of molecular sciences · 2026Article
- Algorithm-driven, phenotype-directed bioactive molecular discovery.Communications chemistry · 2026Article
- ModelistsGCN: a multimodal graph convolutional network framework for single-cell spatial transcriptomic cell typing.Briefings in bioinformatics · 2026Article
- Progress and new challenges in image-based profiling.Molecular systems biology · 2026Review
- Advancing Label-Free Imaging Through CARS Microscopy: From Signal Formation to Biological Interpretation.International journal of molecular sciences · 2026Review
- PhenoProfiler: advancing phenotypic learning for image-based drug discovery.Nature communications · 2025Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In image-based drug discovery, accurately capturing cellular phenotypic responses to chemical perturbations is crucial for understanding drug mechanisms and predicting efficacy. However, existing approaches often depend on complex, multi-step pipelines that are computationally intensive and prone to error. PhenoProfiler addresses these challenges with an efficient, end-to-end deep learning framework that directly transforms high-content, multi-channel cellular images into low-dimensional quantitative representations. Evaluated on nearly 400,000 high-content images and 8.42 million single-cell images, PhenoProfiler consistently outperforms state-of-the-art methods by up to 20% in both accuracy and robustness. Its tailored phenotype correction strategy further emphasizes treatment-induced variations, improving the detection of biologically meaningful and reproducible signals. PhenoProfiler also effectively clusters treatments with shared molecular pathways and biological annotations, facilitating mechanistic interpretation and target discovery. Collectively, PhenoProfiler establishes a scalable, interpretable, and generalizable framework for high-throughput phenotypic profiling, paving the way for next-generation AI-driven drug screening, precision therapeutics, and systems-level understanding of cellular responses.
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.