ArticleNature communications2021
Generative modeling of single-cell time series with PRESCIENT enables prediction of cell trajectories with interventions.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 papers.
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Who cites it
73 citing papers in PubMed.
- Latent causal diffusions for single-cell perturbation modeling.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- DESIGN: a framework for generative AI-enabled crop molecular design-driven breeding.Science China. Life sciences · 2026Review
- Machine learning and computational approaches to model therapeutic response and resistance in diffuse midline glioma.Journal of neuro-oncology · 2026Review
- Cell-level random splits leak group-owned answers in single-cell benchmarks.bioRxiv : the preprint server for biology · 2026Article
- Learning stochastic dynamics and cell-fate landscapes from single-cell snapshots via optimal transport.Science advances · 2026Article
- Article
- Gene-Chronos: parameter-efficient developmental time inference using a pretrained single-cell foundation model.Briefings in bioinformatics · 2026Article
- PROFET predicts continuous gene expression dynamics from scRNA-seq data to elucidate heterogeneity of cancer treatment responses.Cell systems · 2026Article
- Multiscale learning of gene network-driven phenotypic dynamics of single cells.Molecular systems biology · 2026Article
- Sensitivity analysis of cell fate trajectories from single-cell transcriptomics.Bioinformatics (Oxford, England) · 2026Article
- Dynamic Landscape Analysis of cell fate decisions provides predictive models of neural development from single-cell data.PLoS biology · 2026Article
- scTimeBench: a streamlined benchmarking platform for single-cell time-series analysis.Bioinformatics (Oxford, England) · 2026Article
- Interpretation, extrapolation and perturbation of single cells.Nature reviews. Genetics · 2026Review
- Single-Cell and Spatial Omics: Methods and Applications.MedComm · 2026Review
- Branched Schrödinger Bridge Matching.ArXiv · 2026Article
- Artificial intelligence-enabled multi-scale virtual cell: perspective, challenges, and opportunities.Briefings in bioinformatics · 2026Review
- STORIES: learning cell fate landscapes from spatial transcriptomics using optimal transport.Nature methods · 2026Article
- scMix: learning temporal dynamics of gene expression under irregular time intervals.Bioinformatics (Oxford, England) · 2026Article
- Generative models of cell dynamics: from Neural ODEs to flow matching.Communications biology · 2026Review
- VIST: variational inference for single cell time series.Genome biology · 2026Article
13 more citing papers are in PubMed but not listed here.
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3 authors.
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Abstract
Existing computational methods that use single-cell RNA-sequencing (scRNA-seq) for cell fate prediction do not model how cells evolve stochastically and in physical time, nor can they predict how differentiation trajectories are altered by proposed interventions. We introduce PRESCIENT (Potential eneRgy undErlying Single Cell gradIENTs), a generative modeling framework that learns an underlying differentiation landscape from time-series scRNA-seq data. We validate PRESCIENT on an experimental lineage tracing dataset, where we show that PRESCIENT is able to predict the fate biases of progenitor cells in hematopoiesis when accounting for cell proliferation, improving upon the best-performing existing method. We demonstrate how PRESCIENT can simulate trajectories for perturbed cells, recovering the expected effects of known modulators of cell fate in hematopoiesis and pancreatic β cell differentiation. PRESCIENT is able to accommodate complex perturbations of multiple genes, at different time points and from different starting cell populations, and is available at https://github.com/gifford-lab/prescient .
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