ArticleGenome biology2023
siVAE: interpretable deep generative models for single-cell transcriptomes.
Article in Genome biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Computational blueprints for cell fate programming.Stem cell reports · 2026Review
- Leveraging Lineage Barcodes as Natural Augmentations for Contrastive Learning of Cell Fate in scRNA-seq Data.bioRxiv : the preprint server for biology · 2026Article
- PVAED: prior-guided variational autoencoders with diffusion denoising for interpretable single-cell representation learning.Briefings in bioinformatics · 2026Article
- Geometry-aware graph attention networks to explain single-cell chromatin states and gene expression with SEAGALL.Genome biology · 2026Article
- LAIOR: a hyperbolic neural ODE variational framework for interpretable single-cell manifold learning and trajectory inference.Frontiers in genetics · 2026Article
- GAHIB: graph attention VAE with a hyperbolic information bottleneck for biologically structured single-cell representations.Frontiers in genetics · 2026Article
- AI-driven discovery in protein science for immunology and infectious disease research.Frontiers in bioinformatics · 2026Review
- Integrative Learning of Disentangled Representations from Single-Cell RNA-Sequencing Datasets.Computational and structural biotechnology journal · 2026Article
- Fast Fourier transform is a training-free, ultrafast, highly efficient, and fully interpretable approach for epigenomic data compression.Scientific reports · 2025Article
- A mixture of attention experts-embedded flow-based generative model to create synthetic cells in single-cell RNA-Seq datasets.PLoS computational biology · 2025Article
- sciLaMA: A Single-Cell Representation Learning Framework to Leverage Prior Knowledge from Large Language Models.bioRxiv : the preprint server for biology · 2025Article
- Infusing structural assumptions into dimensionality reduction for single-cell RNA sequencing data to identify small gene sets.Communications biology · 2025Article
- MOSim: bulk and single-cell multilayer regulatory network simulator.Briefings in bioinformatics · 2025Article
- Interpretable single-cell factor decomposition using sciRED.Nature communications · 2025Article
- Towards the Next Generation of Data-Driven Therapeutics Using Spatially Resolved Single-Cell Technologies and Generative AI.European journal of immunology · 2025Review
- Uncovering hidden factors of cognitive resilience in Alzheimer's disease using a conditional-Gaussian mixture variational autoencoder.NPJ dementia · 2025Article
- BuDDI: Bulk Deconvolution with Domain Invariance to predict cell-type-specific perturbations from bulk.PLoS computational biology · 2025Article
- Interpretable single-cell factor decomposition using sciRED.bioRxiv : the preprint server for biology · 2024Article
- Mapping the gene space at single-cell resolution with gene signal pattern analysis.Nature computational science · 2024Article
- scPair: Boosting single cell multimodal analysis by leveraging implicit feature selection and single cell atlases.Nature communications · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Neural networks such as variational autoencoders (VAE) perform dimensionality reduction for the visualization and analysis of genomic data, but are limited in their interpretability: it is unknown which data features are represented by each embedding dimension. We present siVAE, a VAE that is interpretable by design, thereby enhancing downstream analysis tasks. Through interpretation, siVAE also identifies gene modules and hubs without explicit gene network inference. We use siVAE to identify gene modules whose connectivity is associated with diverse phenotypes such as iPSC neuronal differentiation efficiency and dementia, showcasing the wide applicability of interpretable generative models for genomic data analysis.
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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.