ReviewFrontiers in molecular biosciences2022
The performance of deep generative models for learning joint embeddings of single-cell multi-omics data.
Review in Frontiers in molecular biosciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed.
- Mapping ovarian cellular and molecular landscape across the lifespan of women: a scoping review.Human reproduction update · 2026Article
- MultiSP deciphers tissue structure and multicellular communication from spatial multi-omics data.Cell genomics · 2026Article
- iAODE for benchmarking and continuum modeling of single-cell chromatin accessibility.Communications biology · 2026Article
- Design, processing, and modeling for longitudinal multiomics microbiome data.Frontiers in cellular and infection microbiology · 2026Review
- Transforming multi-omics data into images for disease classification: A review of techniques and tools.Journal of pathology informatics · 2026Review
- Integrating single-cell RNA-seq datasets with substantial batch effects.BMC genomics · 2025Article
- Can classical statistics and deep learning converge on explainable, causally driven target discovery?DNA research : an international journal for rapid publication of reports on genes and genomes · 2025Review
- A technical review of multi-omics data integration methods: from classical statistical to deep generative approaches.Briefings in bioinformatics · 2025Review
- Engineering the plant microbiome: synthetic community approaches to enhance crop protection.Frontiers in plant science · 2025Review
- scMaui: a widely applicable deep learning framework for single-cell multiomics integration in the presence of batch effects and missing data.BMC bioinformatics · 2024Article
- Panpipes: a pipeline for multiomic single-cell and spatial transcriptomic data analysis.Genome biology · 2024Article
- Integrating single-cell RNA-seq datasets with substantial batch effects.bioRxiv : the preprint server for biology · 2024Article
- Harnessing the power of AI in precision medicine: NGS-based therapeutic insights for colorectal cancer cohort.Frontiers in oncology · 2024Article
- An in-depth comparison of linear and non-linear joint embedding methods for bulk and single-cell multi-omics.Briefings in bioinformatics · 2023Article
- Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent extensions of single-cell studies to multiple data modalities raise new questions regarding experimental design. For example, the challenge of sparsity in single-omics data might be partly resolved by compensating for missing information across modalities. In particular, deep learning approaches, such as deep generative models (DGMs), can potentially uncover complex patterns
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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.