ArticlePatterns (New York, N.Y.)2023
Transcriptomic forecasting with neural ordinary differential equations.
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 18 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed.
- A novel approach to quantify out-of-distribution uncertainty in Neural and Universal Differential Equations.NPJ systems biology and applications · 2026Article
- Interpretation, extrapolation and perturbation of single cells.Nature reviews. Genetics · 2026Review
- 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
- Decoding yeast transcriptional regulation via a data-and mechanism-driven distributed large-scale network model.Synthetic and systems biotechnology · 2025Article
- A comparison of computational methods for expression forecasting.Genome biology · 2025Article
- Multicondition and multimodal temporal profile inference during mouse embryonic development.Genome research · 2025Article
- Identification of models describing gene expression data leveraging machine learning methods.Interface focus · 2025Article
- Article
- Cell state transitions are decoupled from cell division during early embryo development.Nature cell biology · 2024Article
- Multi-condition and multi-modal temporal profile inference during mouse embryonic development.bioRxiv : the preprint server for biology · 2024Article
- Biologically informed NeuralODEs for genome-wide regulatory dynamics.Genome biology · 2024Article
- Leveraging multi-omics data to empower quantitative systems pharmacology in immuno-oncology.Briefings in bioinformatics · 2024Article
- The rise of scientific machine learning: a perspective on combining mechanistic modelling with machine learning for systems biology.Frontiers in systems biology · 2024Review
- Digitize your Biology! Modeling multicellular systems through interpretable cell behavior.bioRxiv : the preprint server for biology · 2023Article
- Simultaneous estimation of gene regulatory network structure and RNA kinetics from single cell gene expression.bioRxiv : the preprint server for biology · 2023Article
- Biologically informed NeuralODEs for genome-wide regulatory dynamics.Research square · 2023Article
- Predictive modeling of stem cell suppression by inflammatory cytokine networks: A synthetic transcriptomic approach for periodontal tissue engineering.Journal of oral biology and craniofacial researchArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Single-cell transcriptomics technologies can uncover changes in the molecular states that underlie cellular phenotypes. However, understanding the dynamic cellular processes requires extending from inferring trajectories from snapshots of cellular states to estimating temporal changes in cellular gene expression. To address this challenge, we have developed a neural ordinary differential-equation-based method, RNAForecaster, for predicting gene expression states in single cells for multiple future time steps in an embedding-independent manner. We demonstrate that RNAForecaster can accurately predict future expression states in simulated single-cell transcriptomic data with cellular tracking over time. We then show that by using metabolic labeling single-cell RNA sequencing (scRNA-seq) data from constitutively dividing cells, RNAForecaster accurately recapitulates many of the expected changes in gene expression during progression through the cell cycle over a 3-day period. Thus, RNAForecaster enables short-term estimation of future expression states in biological systems from high-throughput datasets with temporal information.
Indexed as
Identifiers
What OpenQuestion holds
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.