ArticleNature reviews. Molecular cell biology2024
Harnessing the deep learning power of foundation models in single-cell omics.
Article in Nature reviews. Molecular cell biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 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
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Visible neural networks for multi-omics integration: a critical review.Frontiers in artificial intelligence · 2025Pooled it
- PPP1R14A in male erectile dysfunction: key role in smooth muscle cells.Sexual medicine · 2026Article
- Article
- RegFormer: a single-cell foundation model powered by gene regulatory hierarchies.Nature communications · 2026Article
- The next paradigm in bioinformatics: a review of multi-agent systems and foundational models for end-to-end scientific discovery.Briefings in bioinformatics · 2026Review
- A transcriptomics-native foundation model for universal cell representation and virtual cell synthesis.bioRxiv : the preprint server for biology · 2026Article
- Implementing generative artificial intelligence in precision oncology: safety, governance, and significance.Journal of hematology & oncology · 2026Review
- Article
- Omics-based large language models: A new engine for drug discovery innovation.Acta pharmaceutica Sinica. B · 2026Review
- AUTOENCODIX: a generalized and versatile framework to train and evaluate autoencoders for biological representation learning and beyond.Nature computational science · 2026Article
- Biomaterial-mediated Cell Atlas: an insight from single-cell and spatial transcriptomics.Bioactive materials · 2025Review
- Perspectives on integrating artificial intelligence and single-cell omics for cellular plasticity research.Quantitative biology (Beijing, China) · 2025Article
- Integrating single-cell RNA-seq datasets with substantial batch effects.BMC genomics · 2025Article
- Transformative advances in single-cell omics: a comprehensive review of foundation models, multimodal integration and computational ecosystems.Journal of translational medicine · 2025Review
- Biology-driven insights into the power of single-cell foundation models.Genome biology · 2025Article
- BioLLM: A standardized framework for integrating and benchmarking single-cell foundation models.Patterns (New York, N.Y.) · 2025Article
- GPerturb: Gaussian process modelling of single-cell perturbation data.Nature communications · 2025Article
- CellMemory: hierarchical interpretation of out-of-distribution cells using bottlenecked transformer.Genome biology · 2025Article
- Sex differences in bladder cancer: understanding biological and clinical implications.Biology of sex differences · 2025Review
- Unveiling potential threats: backdoor attacks in single-cell pre-trained models.Cell discovery · 2024Article
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
PubMed holds no abstract for this paper.
Indexed as
Identifiers
38926531What 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.