ArticlePatterns (New York, N.Y.)2023
Generative modeling of single-cell gene expression for dose-dependent chemical perturbations.
Article in Patterns (New York, N.Y.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- A systematic comparison of single-cell perturbation response prediction models.Science advances · 2026Article
- A transcription factor regulatory atlas for activity inference and perturbation prediction.Nucleic acids research · 2026Article
- Gene regulatory networks: from correlative models to causal explanations.Nature reviews. Genetics · 2026Review
- Benchmarking the prediction of responding cells to perturbations affecting both gene expression and cellular abundance using scRNA sequencing.Scientific reports · 2026Article
- scArchon: a scalable benchmarking framework for assessing single-cell perturbation models.Genome biology · 2026Article
- Patches: A Representation Learning Framework for Decoding Shared and Condition-Specific Transcriptional Programs in Wound Healing.bioRxiv : the preprint server for biology · 2026Article
- Illuminating cell states by a comprehensive and interpretable single cell foundation model.Nature communications · 2026Article
- Benchmarking algorithms for generalizable single-cell perturbation response prediction.Nature methods · 2026Article
- An expanded role for single-cell chemical genomics profiling in drug discovery.The Biochemical journal · 2026Review
- AI-Based Prediction of Gene Expression in Single-Cell and Multiscale Genomics and Transcriptomics.International journal of molecular sciences · 2026Review
- AI-guided ethnopharmacology for cardiovascular drug discovery: from biomedical data to experimental validation.Frontiers in pharmacology · 2026Review
- Squidiff: predicting cellular development and responses to perturbations using a diffusion model.Nature methods · 2026Article
- AI-driven virtual cell models in preclinical research: technical pathways, validation mechanisms, and clinical translation potential.NPJ digital medicine · 2025Review
- Decoding cell fate: integrated experimental and computational analysis at the single-cell level.Bioinformatics (Oxford, England) · 2025Review
- Epigenetic synthetic lethality as a cancer therapeutic strategy: synergy of experimental and computational approaches.Epigenomics · 2025Review
- Squidiff: Predicting cellular development and responses to perturbations using a diffusion model.bioRxiv : the preprint server for biology · 2025Article
- scOTM: A Deep Learning Framework for Predicting Single-Cell Perturbation Responses with Large Language Models.Bioengineering (Basel, Switzerland) · 2025Article
- Article
- Application of a metabolic network-based graph neural network for the identification of toxicant-induced perturbations.Toxicological sciences : an official journal of the Society of Toxicology · 2025Article
- Dose-response assessment of dipentyl phthalate effects on testosterone production and morphogenesis of late-gestation fetal rat testis.Environment international · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Single-cell sequencing reveals the heterogeneity of cellular response to chemical perturbations. However, testing all relevant combinations of cell types, chemicals, and doses is a daunting task. A deep generative learning formalism called variational autoencoders (VAEs) has been effective in predicting single-cell gene expression perturbations for single doses. Here, we introduce single-cell variational inference of dose-response (scVIDR), a VAE-based model that predicts both single-dose and multiple-dose cellular responses better than existing models. We show that scVIDR can predict dose-dependent gene expression across mouse hepatocytes, human blood cells, and cancer cell lines. We biologically interpret the latent space of scVIDR using a regression model and use scVIDR to order individual cells based on their sensitivity to chemical perturbation by assigning each cell a "pseudo-dose" value. We envision that scVIDR can help reduce the need for repeated animal testing across tissues, chemicals, and doses.
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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.