ArticleNature communications2025
Predicting cell morphological responses to perturbations using generative modeling.
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 26 papers.
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Who cites it
26 citing papers in PubMed.
- CFM-GP: unified conditional flow matching to learn gene perturbation across cell types.NAR genomics and bioinformatics · 2026Article
- A generative framework for predicting cellular morphological and transcriptomic perturbation responses.Cell reports methods · 2026Article
- Mapping human microglial morphological diversity via handcrafted and deep learning-derived image features.iScience · 2026Article
- Towards the construction of a virtual yeast.Nature · 2026Review
- Integrating chemical structures as treatments improves representations of microscopy images for morphological profiling.PLoS computational biology · 2026Article
- Bacterial gene regulatory neural network as a biocomputing library of mathematical solvers.NPJ systems biology and applications · 2026Article
- Progress and new challenges in image-based profiling.Molecular systems biology · 2026Review
- Gene Expression at the Pluripotency Stage Predicts Pancreatic Endocrine Differentiation in iPSC Clones.Stem cell reviews and reports · 2026Article
- A diffusion model conditioned on compound bioactivity profiles for generating high-content images.Scientific reports · 2026Article
- HistoGWAS: an AI-enabled framework for automated genetic analysis of tissue phenotypes in histology cohorts.Genome biology · 2026Article
- Unlocking the potential of computational phenotypic drug discovery: methods, challenges, and future directions.NPJ systems biology and applications · 2026Review
- Harnessing induced pluripotent stem cells and organoids for disease modeling and precision medicine.Stem cell research & therapy · 2026Review
- Interpretable Thermodynamic Score-based Classification of Relaxation Excursions.bioRxiv : the preprint server for biology · 2025Article
- REECAP: Contrastive learning of retinal aging reveals genetic loci linking morphology to eye disease.medRxiv : the preprint server for health sciences · 2025Article
- Transforming microfluidics for single-cell analysis with robotics and artificial intelligence.Lab on a chip · 2025Review
- Prediction of cellular morphology changes under perturbations with a transcriptome-guided diffusion model.Nature communications · 2025Article
- Article
- Integrating Biological Knowledge for Robust Microscopy Image Profiling onProceedings. IEEE International Conference on Computer Vision · 2025Article
- MorphoDiff: Cellular Morphology Painting with Diffusion Models.bioRxiv : the preprint server for biology · 2024Article
- A mini-review on perturbation modelling across single-cell omic modalities.Computational and structural biotechnology journal · 2024Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Advancements in high-throughput screenings enable the exploration of rich phenotypic readouts through high-content microscopy, expediting the development of phenotype-based drug discovery. However, analyzing large and complex high-content imaging screenings remains challenging due to incomplete sampling of perturbations and the presence of technical variations between experiments. To tackle these shortcomings, we present IMage Perturbation Autoencoder (IMPA), a generative style-transfer model predicting morphological changes of perturbations across genetic and chemical interventions. We show that IMPA accurately captures morphological and population-level changes of both seen and unseen perturbations on breast cancer and osteosarcoma cells. Additionally, IMPA accounts for batch effects and can model perturbations across various sources of technical variation, further enhancing its robustness in diverse experimental conditions. With the increasing availability of large-scale high-content imaging screens generated by academic and industrial consortia, we envision that IMPA will facilitate the analysis of microscopy data and enable efficient experimental design via in-silico perturbation prediction.
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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 OpenQuestion graph.