ReviewNature methods2024
Transformers in single-cell omics: a review and new perspectives.
Review in Nature methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 97 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
97 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Current progress and emerging trends of car-based immunotherapy for hepatocellular carcinoma: a data-driven atlas from multidatabase integration.Frontiers in immunology · 2026Pooled it
- scLDM: a conditional diffusion framework for single-cell perturbation prediction.Bioinformatics (Oxford, England) · 2026Article
- Multi‑omics insights into uveitis: From mechanisms to precision medicine (Review).International journal of molecular medicine · 2026Review
- Multimodal learning enables chat-based exploration of single-cell data.Nature biotechnology · 2026Article
- Article
- scTransMIL bridges patient-level disease states and single-cell transcriptomics for cancer screening and heterogeneity inference.Nature communications · 2026Article
- Advancing the Deciphering of Host-Microbe Crosstalk with Spatial Omics: A Mini-Review.Current microbiology · 2026Review
- A systematic assessment of single-cell language model configurations.NAR genomics and bioinformatics · 2026Article
- Systematic benchmarking and optimal strategy selection of cross-species integration methods.Briefings in bioinformatics · 2026Article
- Benchmarking biomedical foundation models.Nature methods · 2026Review
- Clifti-GPT: privacy-preserving federated fine-tuning and transferable inference of foundation models on clinical single-cell data.BioData mining · 2026Article
- Toward generalizable and interpretable AI in regulatory genomics.Nature genetics · 2026Review
- Integrated RNA Sequencing and Multi-Bioinformatics Analyses Reveal the Molecular Mechanisms Underlying the Anti-Breast Cancer Effects of the Fritillariae Thunbergii Bulbus-Bolbostemmatis Rhizoma Herb Pair.Cell biochemistry and function · 2026Article
- Transcriptional landscape of direct reprogramming toward the hematopoietic lineage.bioRxiv : the preprint server for biology · 2026Article
- Cross-species integration of single-cell data reveals conserved pathology-associated cell populations across animal models and human samples.Cell reports methods · 2026Article
- Article
- Advancing AI for multi-omics and clinical data integration in basic and translational cancer research.Nature reviews. Cancer · 2026Review
- A transformer-based language model reveals developmental constraint and network complexity during zebrafish embryogenesis.PNAS nexus · 2026Article
- Tissueformer: extending single-cell foundation models to predict population-level phenotypes.BMC bioinformatics · 2026Article
- Cancer stem cell plasticity in shaping drug resistance landscapes in prostate cancer.Journal of advanced research · 2026Review
37 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Recent efforts to construct reference maps of cellular phenotypes have expanded the volume and diversity of single-cell omics data, providing an unprecedented resource for studying cell properties. Despite the availability of rich datasets and their continued growth, current single-cell models are unable to fully capitalize on the information they contain. Transformers have become the architecture of choice for foundation models in other domains owing to their ability to generalize to heterogeneous, large-scale datasets. Thus, the question arises of whether transformers could set off a similar shift in the field of single-cell modeling. Here we first describe the transformer architecture and its single-cell adaptations and then present a comprehensive review of the existing applications of transformers in single-cell analysis and critically discuss their future potential for single-cell biology. By studying limitations and technical challenges, we aim to provide a structured outlook for future research directions at the intersection of machine learning and single-cell biology.
Indexed as
Identifiers
39122952What OpenQuestion holds
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.