ArticleJournal of biomolecular screening2013
Comparison of methods for image-based profiling of cellular morphological responses to small-molecule treatment.
Article in Journal of biomolecular screening, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 93 papers.
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Who cites it
93 citing papers in PubMed.
- A High-Content Imaging Pipeline to Investigate Subcytotoxic Effects in RTgill-W1 Cells.Environmental science & technology · 2026Article
- Integrating chemical structures as treatments improves representations of microscopy images for morphological profiling.PLoS computational biology · 2026Article
- Impact of regularization methods and outlier removal on unsupervised sample classification.BMC bioinformatics · 2026Article
- Progress and new challenges in image-based profiling.Molecular systems biology · 2026Review
- Unlocking the potential of computational phenotypic drug discovery: methods, challenges, and future directions.NPJ systems biology and applications · 2026Review
- Multiplexed high-content imaging uncovers morphological diversity of lymphocyte activation and dysfunction.bioRxiv : the preprint server for biology · 2026Article
- A pooled Cell Painting CRISPR screening platform enables de novo inference of gene function by self-supervised deep learning.Nature communications · 2025Article
- PhenoProfiler: advancing phenotypic learning for image-based drug discovery.Nature communications · 2025Article
- Cell-DINO: Self-supervised image-based embeddings for cell fluorescent microscopy.PLoS computational biology · 2025Article
- Extracting regions of lipid droplets from confocal microscopy images utilizing optical properties of oleaginous yeast.Applied microbiology and biotechnology · 2025Article
- Article
- DeepHVI: A multimodal deep learning framework for predicting human-virus protein-protein interactions using protein language models.Biosafety and health · 2025Article
- Cross modality learning of cell painting and transcriptomics data improves mechanism of action clustering and bioactivity modelling.Scientific reports · 2025Article
- Self-supervision advances morphological profiling by unlocking powerful image representations.Scientific reports · 2025Article
- Predicting cell morphological responses to perturbations using generative modeling.Nature communications · 2025Article
- Attention-based deep learning for accurate cell image analysis.Scientific reports · 2025Article
- uniDINO: Assay-independent feature extraction for fluorescence microscopy images.Computational and structural biotechnology journal · 2025Article
- Drug response in the era of precision medicine: A methodological review.Computational and structural biotechnology journal · 2025Review
- Unveiling endothelial cell border heterogeneity: VE-cadherin adherens junction stratification by deep convolutional neural networks.PloS one · 2025Article
- Systematic data analysis pipeline for quantitative morphological cell phenotyping.Computational and structural biotechnology journal · 2024Review
33 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
Quantitative microscopy has proven a versatile and powerful phenotypic screening technique. Recently, image-based profiling has shown promise as a means for broadly characterizing molecules' effects on cells in several drug-discovery applications, including target-agnostic screening and predicting a compound's mechanism of action (MOA). Several profiling methods have been proposed, but little is known about their comparative performance, impeding the wider adoption and further development of image-based profiling. We compared these methods by applying them to a widely applicable assay of cultured cells and measuring the ability of each method to predict the MOA of a compendium of drugs. A very simple method that is based on population means performed as well as methods designed to take advantage of the measurements of individual cells. This is surprising because many treatments induced a heterogeneous phenotypic response across the cell population in each sample. Another simple method, which performs factor analysis on the cellular measurements before averaging them, provided substantial improvement and was able to predict MOA correctly for 94% of the treatments in our ground-truth set. To facilitate the ready application and future development of image-based phenotypic profiling methods, we provide our complete ground-truth and test data sets, as well as open-source implementations of the various methods in a common software framework.
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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.