ArticleBioinformatics (Oxford, England)2024
scNODE : generative model for temporal single cell transcriptomic data prediction.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Learning stochastic dynamics and cell-fate landscapes from single-cell snapshots via optimal transport.Science advances · 2026Article
- scTimeBench: a streamlined benchmarking platform for single-cell time-series analysis.Bioinformatics (Oxford, England) · 2026Article
- From Prediction to Creation: Generative Plant Design.Plants (Basel, Switzerland) · 2026Review
- Inferring stochastic dynamics by biophysical Neural ODE using single-cell transcriptomics.Nature communications · 2026Article
- Advancing single-cell omics and cell-based therapeutics with quantum computing.Nature reviews. Molecular cell biology · 2026Review
- iAODE for benchmarking and continuum modeling of single-cell chromatin accessibility.Communications biology · 2026Article
- 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
- VIST: variational inference for single cell time series.Genome biology · 2026Article
- AI-Based Prediction of Gene Expression in Single-Cell and Multiscale Genomics and Transcriptomics.International journal of molecular sciences · 2026Review
- Learning collective multicellular dynamics with an interacting mean field neural SDE model.PLoS computational biology · 2026Article
- Deciphering cell-fate trajectories using spatiotemporal single-cell transcriptomic data.NPJ systems biology and applications · 2025Review
- Multicondition and multimodal temporal profile inference during mouse embryonic development.Genome research · 2025Article
- Variational inference of single cell time series.bioRxiv : the preprint server for biology · 2025Article
- GNODEVAE: a graph-based ODE-VAE enhances clustering for single-cell data.BMC genomics · 2025Article
- scMultiNODE: Integrative and Scalable Framework for Multi-Modal Temporal Single-Cell Data.bioRxiv : the preprint server for biology · 2025Article
- ARTEMIS integrates autoencoders and Schrödinger Bridges to predict continuous dynamics of gene expression, cell population, and perturbation from time-series single-cell data.Bioinformatics (Oxford, England) · 2025Article
- Integrating Dynamical Systems Modeling with Spatiotemporal scRNA-Seq Data Analysis.Entropy (Basel, Switzerland) · 2025Review
- Multi-condition and multi-modal temporal profile inference during mouse embryonic development.bioRxiv : the preprint server for biology · 2024Article
- The rise of scientific machine learning: a perspective on combining mechanistic modelling with machine learning for systems biology.Frontiers in systems biology · 2024Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
summaryMeasurement of single-cell gene expression at different timepoints enables the study of cell development. However, due to the resource constraints and technical challenges associated with the single-cell experiments, researchers can only profile gene expression at discrete and sparsely sampled timepoints. This missing timepoint information impedes downstream cell developmental analyses. We propose scNODE, an end-to-end deep learning model that can predict in silico single-cell gene expression at unobserved timepoints. scNODE integrates a variational autoencoder with neural ordinary differential equations to predict gene expression using a continuous and nonlinear latent space. Importantly, we incorporate a dynamic regularization term to learn a latent space that is robust against distribution shifts when predicting single-cell gene expression at unobserved timepoints. Our evaluations on three real-world scRNA-seq datasets show that scNODE achieves higher predictive performance than state-of-the-art methods. We further demonstrate that scNODE's predictions help cell trajectory inference under the missing timepoint paradigm and the learned latent space is useful for in silico perturbation analysis of relevant genes along a developmental cell path. AVAILABILITY AND IMPLEMENTATION: The data and code are publicly available at https://github.com/rsinghlab/scNODE.
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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.