ArticleCell death discovery2023
CellDeathPred: a deep learning framework for ferroptosis and apoptosis prediction based on cell painting.
Article in Cell death discovery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Programmed cell death in inflammation: interactions and holistic perspective.Cellular & molecular immunology · 2026Review
- Deciphering the rules of regulated cell death subroutines by in silico methods.Journal of advanced research · 2026Review
- Artificial intelligence-enabled cytopathological interpretation of fluorescence microscopy images via a multimodal large language model.Quantitative biology (Beijing, China) · 2026Article
- Review
- xNNPCD identifies regulators of programmed cell death by integrating perturbation transcriptomes with cancer dependency profiles.bioRxiv : the preprint server for biology · 2026Article
- Decoding the spatiotemporal characteristics of ferroptosis: reshaping tumour therapeutic strategies.Experimental hematology & oncology · 2026Review
- Large scale compound selection guided by cell painting reveals activity cliffs and functional relationships.Communications biology · 2026Article
- Humanized hiPSC Platforms for I/R Injury: Advancing Toward Precision Cardioprotection.Cardiovascular therapeutics · 2026Review
- Navigating Transition Metal-Dependent Cell Death: Mechanisms, Crosstalk, and Future Directions.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Big data and AI: Potential and challenges for digital transformation in toxicology.Environmental analysis, health and toxicology · 2025Article
- A morphology and secretome map of pyroptosis.Molecular biology of the cell · 2025Article
- Deciphering Ferroptosis: From Molecular Pathways to Machine Learning-Guided Therapeutic Innovation.Molecular biotechnology · 2025Review
- Reproducible image-based profiling with Pycytominer.Nature methods · 2025Article
- CPHNet: a novel pipeline for anti-HAPE drug screening via deep learning-based Cell Painting scoring.Respiratory research · 2025Article
- Harnessing ferroptosis for precision oncology: challenges and prospects.BMC biology · 2025Review
- Single-cell morphological tracking of cell states to identify small-molecule modulators of liver differentiation.iScience · 2025Article
- Applications of Artificial Intelligence, Deep Learning, and Machine Learning to Support the Analysis of Microscopic Images of Cells and Tissues.Journal of imaging · 2025Review
- Drug-induced cytotoxicity prediction in muscle cells, an application of the Cell Painting assay.PloS one · 2025Article
- Insights into the Identification of iPSC- and Monocyte-Derived Macrophage-Polarizing Compounds by AI-Fueled Cell Painting Analysis Tools.International journal of molecular sciences · 2024Article
- Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cell death, such as apoptosis and ferroptosis, play essential roles in the process of development, homeostasis, and pathogenesis of acute and chronic diseases. The increasing number of studies investigating cell death types in various diseases, particularly cancer and degenerative diseases, has raised hopes for their modulation in disease therapies. However, identifying the presence of a particular cell death type is not an obvious task, as it requires computationally intensive work and costly experimental assays. To address this challenge, we present CellDeathPred, a novel deep-learning framework that uses high-content imaging based on cell painting to distinguish cells undergoing ferroptosis or apoptosis from healthy cells. In particular, we incorporate a deep neural network that effectively embeds microscopic images into a representative and discriminative latent space, classifies the learned embedding into cell death modalities, and optimizes the whole learning using the supervised contrastive loss function. We assessed the efficacy of the proposed framework using cell painting microscopy data sets from human HT-1080 cells, where multiple inducers of ferroptosis and apoptosis were used to trigger cell death. Our model confidently separates ferroptotic and apoptotic cells from healthy controls, with an average accuracy of 95% on non-confocal data sets, supporting the capacity of the CellDeathPred framework for cell death discovery.
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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.