ArticleGenome biology2025
scVAEDer: integrating deep diffusion models and variational autoencoders for single-cell transcriptomics analysis.
Article in Genome biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Hundreds of cardiac MRI traits derived using 3D diffusion autoencoders share a common genetic architecture.Nature communications · 2026Article
- Predicting condition-aware drug-induced transcriptional responses via a latent diffusion model.Bioinformatics (Oxford, England) · 2026Article
- Squidiff: predicting cellular development and responses to perturbations using a diffusion model.Nature methods · 2026Article
- Interpretable Thermodynamic Score-based Classification of Relaxation Excursions.bioRxiv : the preprint server for biology · 2025Article
- Squidiff: Predicting cellular development and responses to perturbations using a diffusion model.bioRxiv : the preprint server for biology · 2025Article
- Discovering governing equations of biological systems through representation learning and sparse model discovery.NAR genomics and bioinformatics · 2025Article
- scVAEDer: integrating deep diffusion models and variational autoencoders for single-cell transcriptomics analysis.Genome biology · 2025Article
- BioDSNN: a dual-stream neural network with hybrid biological knowledge integration for multi-gene perturbation response prediction.Briefings in bioinformatics · 2024Article
- FateNet: an integration of dynamical systems and deep learning for cell fate prediction.Bioinformatics (Oxford, England) · 2024Article
- Fatecode enables cell fate regulator prediction using classification-supervised autoencoder perturbation.Cell reports methods · 2024Article
- Analyzing scRNA-seq data by CCP-assisted UMAP and tSNE.PloS one · 2024Article
- Adversarial training improves model interpretability in single-cell RNA-seq analysis.Bioinformatics advances · 2023Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Discovering a lower-dimensional embedding of single-cell data can improve downstream analysis. The embedding should encapsulate both the high-level features and low-level variations. While existing generative models attempt to learn such low-dimensional representations, they have limitations. Here, we introduce scVAEDer, a scalable deep-learning model that combines the power of variational autoencoders and deep diffusion models to learn a meaningful representation that retains both global structure and local variations. Using the learned embeddings, scVAEDer can generate novel scRNA-seq data, predict perturbation response on various cell types, identify changes in gene expression during dedifferentiation, and detect master regulators in biological processes.
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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.