Article

ARTEMIS integrates autoencoders and Schrödinger Bridges to predict continuous dynamics of gene expression, cell population and perturbation from time-series single-cell data

2025-01-26

Abstract excerpt

Cellular processes like development, differentiation, and disease progression are highly complex and dynamic (e.g., gene expression). These processes often un-dergo cell population changes driven by cell birth, proliferation, and death. Single-cell sequencing enables gene expression measurement at the cellular resolution, allowing us to decipher cellular and molecular dynamics underlying these pro-cesses. However,...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 0e777999-72fb-58bd-a82b-d95faf229316
DOI: 10.1101/2025.01.23.634618

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

ARTEMIS integrates autoencoders and Schrödinger Bridges to predict continuous dynamics of gene expression, cell population and perturbation from time-series single-cell dataDOI 10.1101/2025.01.23.634618Read

VAPOR: A variational autoencoder with transport operators to disentangle cellular gene expression dynamics of co-occurring biological processes in time and spaceSemantic proximity Read

scDiffEq: drift-diffusion modeling of single-cell dynamics with neural stochastic differential equationsSemantic proximity Read

scMEDAL for the interpretable analysis of single-cell transcriptomics data with batch effect visualization using a deep mixed effects autoencoderSemantic proximity Read

Squidiff: Predicting cellular development and responses to perturbations using a diffusion modelSemantic proximity Read

scMTG reconstructs single-cell temporal dynamics with Markov transition generatorsSemantic proximity Read

<i>ACTIVA</i> : realistic single-cell RNA-seq generation with automatic cell-type identification using introspective variational autoencodersSemantic proximity Read

SIDISH Identifies High-Risk Disease-Associated Cells and Biomarkers by Integrating Single-Cell Depth and Bulk BreadthSemantic proximity Read

Latent periodic process inference from single-cell RNA-seq dataSemantic proximity Read

Generating and navigating single cell dynamics via a geodesic bridge between nonlinear transcriptional and linear latent manifoldsSemantic proximity Read

Global cell-state and gene-program representations reveal conserved and context-specific perturbation responses of cellsSemantic proximity Read

Generating Synthetic Single Cell Data from Bulk RNA-seq Using a Pretrained Variational AutoencoderSemantic proximity Read