Quantitative characterization of cell niches in spatially resolved omics data.

Predicting cell morphological responses to perturbations using generative modeling.

Towards multimodal foundation models in molecular cell biology.

Integration and querying of multimodal single-cell data with PoE-VAE.

Integrating multi-covariate disentanglement with counterfactual analysis on synthetic data enables cell type discovery and counterfactual predictions.

Mapping and reprogramming human tissue microenvironments with MintFlow.

Multi-modal generative modeling for joint analysis of single-cell T cell receptor and gene expression data.

A single cell and spatial genomics atlas of human skin fibroblasts in health and disease.

ArchMap: A web-based platform for reference-based analysis of single-cell datasets.

Pertpy: an end-to-end framework for perturbation analysis.

Toward learning a foundational representation of cells and genes.

The future of rapid and automated single-cell data analysis using reference mapping.

Deep learning in spatially resolved transcriptomics: a comprehensive technical view.

Multimodal weakly supervised learning to identify disease-specific changes in single-cell atlases.

Deep generative modeling of transcriptional dynamics for RNA velocity analysis in single cells.

An integrated single-cell reference atlas of the human endometrium.

Population-level integration of single-cell datasets enables multi-scale analysis across samples.

Single-cell reference mapping to construct and extend cell-type hierarchies.

Best practices for single-cell analysis across modalities.

An integrated cell atlas of the human lung in health and disease.

Predicting cellular responses to complex perturbations in high‐throughput screens.

The scverse project provides a computational ecosystem for single-cell omics data analysis.

Mapping cells to gene programs.

Biologically informed deep learning to query gene programs in single-cell atlases.

Modelling method using a conditional variational autoencoder.

Squidpy: a scalable framework for spatial omics analysis.

A Python library for probabilistic analysis of single-cell omics data.

Mapping single-cell data to reference atlases by transfer learning.

Machine learning for perturbational single-cell omics.

Conditional out-of-distribution generation for unpaired data using transfer VAE.

scGen predicts single-cell perturbation responses.

Deep packet: A novel approach for encrypted traffic classification using deep learning.