ArticleNature communications2021
VEGA is an interpretable generative model for inferring biological network activity in single-cell transcriptomics.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers, 2 of them syntheses that pooled it.
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Who cites it
65 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Visible neural networks for multi-omics integration: a critical review.Frontiers in artificial intelligence · 2025Pooled it
- A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data.BMC bioinformatics · 2023Pooled it
- 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
- Article
- scENature communications · 2026Article
- In silico generation of gene expression profiles using diffusion models.BMC bioinformatics · 2026Article
- scArchon: a scalable benchmarking framework for assessing single-cell perturbation models.Genome biology · 2026Article
- PVAED: prior-guided variational autoencoders with diffusion denoising for interpretable single-cell representation learning.Briefings in bioinformatics · 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
- Representation learning of single-cell RNA-seq data.RNA (New York, N.Y.) · 2026Review
- Using Biological Networks to Guide Biomedical Prediction.Methods in molecular biology (Clifton, N.J.) · 2026Article
- AUTOENCODIX: a generalized and versatile framework to train and evaluate autoencoders for biological representation learning and beyond.Nature computational science · 2026Article
- Visualizing the multidimensional landscape of biological variation in modern microscopy.Frontiers in bioinformatics · 2026Article
- Integrative Learning of Disentangled Representations from Single-Cell RNA-Sequencing Datasets.Computational and structural biotechnology journal · 2026Article
- Article
- Decoding cell fate: integrated experimental and computational analysis at the single-cell level.Bioinformatics (Oxford, England) · 2025Review
- Recovering time-varying networks from single-cell data.Bioinformatics (Oxford, England) · 2025Article
- Beyond the black box with biologically informed neural networks.Nature reviews. Genetics · 2025Article
- IRnet: Immunotherapy response prediction using pathway knowledge-informed graph neural network.Journal of advanced research · 2025Article
5 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors.
Funding
Abstract
Deep learning architectures such as variational autoencoders have revolutionized the analysis of transcriptomics data. However, the latent space of these variational autoencoders offers little to no interpretability. To provide further biological insights, we introduce a novel sparse Variational Autoencoder architecture, VEGA (VAE Enhanced by Gene Annotations), whose decoder wiring mirrors user-provided gene modules, providing direct interpretability to the latent variables. We demonstrate the performance of VEGA in diverse biological contexts using pathways, gene regulatory networks and cell type identities as the gene modules that define its latent space. VEGA successfully recapitulates the mechanism of cellular-specific response to treatments, the status of master regulators as well as jointly revealing the cell type and cellular state identity in developing cells. We envision the approach could serve as an explanatory biological model for development and drug treatment experiments.
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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.