ArticleNature cell biology2023
Biologically informed deep learning to query gene programs in single-cell atlases.
Article in Nature cell biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 77 papers, 1 of them a synthesis that pooled it.
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Who cites it
77 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Visible neural networks for multi-omics integration: a critical review.Frontiers in artificial intelligence · 2025Pooled it
- A distinct effector B cell population drives autoantibody production in SARS-CoV-2 infection.Immunity · 2026Article
- The lipidomic architecture of the mouse brain.Nature · 2026Article
- Target discovery and drug design in the era of artificial intelligence.Medicinal chemistry research : an international journal for rapid communications on design and mechanisms of action of biologically active agents · 2026Review
- A Gene-Program Architecture of Mouse T cells.bioRxiv : the preprint server for biology · 2026Article
- Article
- TSTScope Unifies Single-Cell Multi-Omics to Identify Functional T Cell States Predictive of Immunotherapy Response.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Hierarchical classification of immune cell transcriptomes at population-scale.bioRxiv : the preprint server for biology · 2026Article
- Supervised deep learning with gene functional annotation for cell classification.PLoS computational biology · 2026Article
- Interpretation, extrapolation and perturbation of single cells.Nature reviews. Genetics · 2026Review
- IST: an ontology-guided attention-based autoencoder for interpretable analysis of single-cell transcriptomic data.Scientific reports · 2026Article
- scMILD: Single-cell multiple instance learning for sample classification and associated subpopulation discovery.iScience · 2026Article
- A sequence knowledge-guided deep learning method for single-cell multi-omics translation.Genome biology · 2026Article
- Robust integration and annotation of single-cell and spatial omics data using interpretable gene programs.Cell genomics · 2026Article
- Quantifying stability via count splitting to guide model selection in RNA velocity analyses.bioRxiv : the preprint server for biology · 2026Article
- NeMO Analytics: a compendium of transcriptomic data for the exploration of neocortical development.Nature neuroscience · 2026Article
- CellPolaris: Transfer Learning for Gene Regulatory Network Construction to Guide Cell State Transitions.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- AUTOENCODIX: a generalized and versatile framework to train and evaluate autoencoders for biological representation learning and beyond.Nature computational science · 2026Article
- Quantifying stability via count splitting to guide model selection in RNA velocity analyses.Bioinformatics advances · 2026Article
- Using Biological Networks to Guide Biomedical Prediction.Methods in molecular biology (Clifton, N.J.) · 2026Article
17 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
The increasing availability of large-scale single-cell atlases has enabled the detailed description of cell states. In parallel, advances in deep learning allow rapid analysis of newly generated query datasets by mapping them into reference atlases. However, existing data transformations learned to map query data are not easily explainable using biologically known concepts such as genes or pathways. Here we propose expiMap, a biologically informed deep-learning architecture that enables single-cell reference mapping. ExpiMap learns to map cells into biologically understandable components representing known 'gene programs'. The activity of each cell for a gene program is learned while simultaneously refining them and learning de novo programs. We show that expiMap compares favourably to existing methods while bringing an additional layer of interpretability to integrative single-cell analysis. Furthermore, we demonstrate its applicability to analyse single-cell perturbation responses in different tissues and species and resolve responses of patients who have coronavirus disease 2019 to different treatments across cell types.
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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.