ArticleAdvances in neural information processing systems2022
Manifold Interpolating Optimal-Transport Flows for Trajectory Inference.
Article in Advances in neural information processing systems, 2022. 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, 24 citations in OpenAlex.
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- 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
- Branched Schrödinger Bridge Matching.ArXiv · 2026Article
- stVCR: spatiotemporal dynamics of single cells.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
- 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
- Cell lineage tracing: Methods, applications, and challenges.Quantitative biology (Beijing, China) · 2025Review
- Beyond metaphor: quantitative reconstruction of Waddington landscape and exploration of cellular behavior.Briefings in bioinformatics · 2025Review
- Multicondition and multimodal temporal profile inference during mouse embryonic development.Genome research · 2025Article
- Quantifying Landscape and Flux from Single-Cell Omics: Unraveling the Physical Mechanisms of Cell Function.JACS Au · 2025Review
- Identification of models describing gene expression data leveraging machine learning methods.Interface focus · 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 Artificial Intelligence in Next-Generation Sequencing: Advances, Challenges, and Future Directions.Current issues in molecular biology · 2025Review
- Discovering governing equations of biological systems through representation learning and sparse model discovery.NAR genomics and bioinformatics · 2025Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
We present a method called Manifold Interpolating Optimal-Transport Flow (MIOFlow) that learns stochastic, continuous population dynamics from static snapshot samples taken at sporadic timepoints. MIOFlow combines dynamic models, manifold learning, and optimal transport by training neural ordinary differential equations (Neural ODE) to interpolate between static population snapshots as penalized by optimal transport with manifold ground distance. Further, we ensure that the flow follows the geometry by operating in the latent space of an autoencoder that we call a geodesic autoencoder (GAE). In GAE the latent space distance between points is regularized to match a novel multiscale geodesic distance on the data manifold that we define. We show that this method is superior to normalizing flows, Schrödinger bridges and other generative models that are designed to flow from noise to data in terms of interpolating between populations. Theoretically, we link these trajectories with dynamic optimal transport. We evaluate our method on simulated data with bifurcations and merges, as well as scRNA-seq data from embryoid body differentiation, and acute myeloid leukemia treatment.
Identifiers
37397786PMC10312391W4382918912What 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.