ReviewBriefings in bioinformatics2023
Multimodal deep learning approaches for single-cell multi-omics data integration.
Review in Briefings in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
42 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective.Briefings in bioinformatics · 2025Pooled it
- Topology-guided dual-masked contrastive learning for weakly supervised cell-type annotation in single-cell multi-omics.Briefings in bioinformatics · 2026Article
- Comprehensive overview of AI methodologies in nano-drug delivery Optimization and Design.NPJ precision oncology · 2026Review
- Harnessing human tumor organoids for cancer modeling and precision therapy.Protein & cell · 2026Review
- The human microbiome as a source of novel bioactive natural products: structures, bioactivities, and biosynthetic insights.Journal of natural medicines · 2026Review
- MAMMAL - Molecular Aligned Multi-Modal Architecture and Language for biomedical discovery.npj drug discovery · 2026Article
- IST: an ontology-guided attention-based autoencoder for interpretable analysis of single-cell transcriptomic data.Scientific reports · 2026Article
- Explainable graph learning for multimodal single-cell data integration.BMC bioinformatics · 2026Article
- A deep adversarial network model for multi-task analysis of single-cell omics data.Briefings in bioinformatics · 2026Article
- scII: Dual-Threshold Adaptive Integration of Single-Cell Multiomics Data Driven by Imputation.Journal of chemical information and modeling · 2026Article
- Machine learning for extracellular vesicles enables diagnostic and therapeutic nanobiotechnology.Journal of nanobiotechnology · 2026Review
- Clustering single-cell multi-omics data via multi-subspace contrastive learning with structural smoothness.Briefings in bioinformatics · 2026Article
- Review
- From cellular heterogeneity to precision medicine: single-cell multi-omics in CNS disease research.Frontiers in cellular neuroscience · 2026Review
- Multi-view deep learning of highly multiplexed imaging data improves association of cell states with clinical outcomes.Bioinformatics advances · 2026Article
- ZebraMap: A Multimodal Rare Disease Knowledge Map with Automated Data Aggregation & LLM-Enriched Information Extraction Pipeline.Diagnostics (Basel, Switzerland) · 2025Article
- Bioinformatics and artificial intelligence in genomic data analysis: current advances and future directions.Molecular genetics and genomics : MGG · 2025Review
- Integrated single-cell multi-omics profiling reveals a senescence-associated hematopoietic landscape and regulatory network in aging bone marrow.Biogerontology · 2025Article
- A novel modality contribution confidence-enhanced multimodal deep learning framework for multiomics data.BMC bioinformatics · 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
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Integrating single-cell multi-omics data is a challenging task that has led to new insights into complex cellular systems. Various computational methods have been proposed to effectively integrate these rapidly accumulating datasets, including deep learning. However, despite the proven success of deep learning in integrating multi-omics data and its better performance over classical computational methods, there has been no systematic study of its application to single-cell multi-omics data integration. To fill this gap, we conducted a literature review to explore the use of multimodal deep learning techniques in single-cell multi-omics data integration, taking into account recent studies from multiple perspectives. Specifically, we first summarized different modalities found in single-cell multi-omics data. We then reviewed current deep learning techniques for processing multimodal data and categorized deep learning-based integration methods for single-cell multi-omics data according to data modality, deep learning architecture, fusion strategy, key tasks and downstream analysis. Finally, we provided insights into using these deep learning models to integrate multi-omics data and better understand single-cell biological mechanisms.
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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.