ArticleNature communications2024
multiDGD: A versatile deep generative model for multi-omics data.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Adversarial random forests for omics synthesis.bioRxiv : the preprint server for biology · 2026Article
- Mapping ovarian cellular and molecular landscape across the lifespan of women: a scoping review.Human reproduction update · 2026Article
- Multi-omics-driven precision medicine.iMeta · 2026Review
- Generalist biological artificial intelligence in modeling the language of life.Nature biotechnology · 2026Review
- Interpretable modality-aware mapping of gene regulation in single-cell multiomics with scMAGCA.Nature communications · 2026Article
- Mapping whole-organism genetic comorbidities across model Species using unified ontologies.Genetics · 2026Article
- From classical machine learning to emerging foundation models: review on multimodal data integration for cancer research.Artificial intelligence review · 2026Article
- AI-driven multiscale virtual plant cell modeling: from molecular mechanisms to tissue functions.Planta · 2026Review
- A review of multi-omics integration techniques across five machine learning method families.Bioinformatics advances · 2026Review
- GCOA-Net: a graph-regularized cross-omics attention network for interpretable breast cancer molecular subtype classification.Frontiers in medicine · 2026Article
- Transformative advances in single-cell omics: a comprehensive review of foundation models, multimodal integration and computational ecosystems.Journal of translational medicine · 2025Review
- Gut microbiota and tuberculosis.iMeta · 2025Review
- Protein lipidation in the tumor microenvironment: enzymology, signaling pathways, and therapeutics.Molecular cancer · 2025Review
- Dual-Faced Role of GDF6 in Cancer: Mechanistic Insights into Its Context-Dependent Regulation of Metastasis and Immune Evasion Across Human Malignancies.Current issues in molecular biology · 2025Article
- Editorial: The Human Cell Atlas. What Is It and Where Could It Take Us?Medical science monitor : international medical journal of experimental and clinical research · 2025Article
- Review
- multiDGD: A versatile deep generative model for multi-omics data.Nature communications · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Recent technological advancements in single-cell genomics have enabled joint profiling of gene expression and alternative modalities at unprecedented scale. Consequently, the complexity of multi-omics data sets is increasing massively. Existing models for multi-modal data are typically limited in functionality or scalability, making data integration and downstream analysis cumbersome. We present multiDGD, a scalable deep generative model providing a probabilistic framework to learn shared representations of transcriptome and chromatin accessibility. It shows outstanding performance on data reconstruction without feature selection. We demonstrate on several data sets from human and mouse that multiDGD learns well-clustered joint representations. We further find that probabilistic modeling of sample covariates enables post-hoc data integration without the need for fine-tuning. Additionally, we show that multiDGD can detect statistical associations between genes and regulatory regions conditioned on the learned representations. multiDGD is available as an scverse-compatible package on GitHub.
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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.