ArticleGenome biology2025
scExtract: leveraging large language models for fully automated single-cell RNA-seq data annotation and prior-informed multi-dataset integration.
Article in Genome biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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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.
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Who cites it
9 citing papers in PubMed.
- A multimodal predictive model incorporating transcriptomic-guided blood biomarkers and clinical variables for sepsis-associated acute kidney injury.Renal failure · 2026Article
- CellTypeAI: cell annotation for scRNA-seq using local generative-AI.Bioinformatics (Oxford, England) · 2026Article
- Deciphering the cellular landscape of pathological scars.Burns & trauma · 2026Article
- Family-Specialized Transformer for L-cystathionine gamma-lyase Engineering and Its Structural Interpretation.Computational and structural biotechnology journal · 2026Article
- Biomaterial-mediated Cell Atlas: an insight from single-cell and spatial transcriptomics.Bioactive materials · 2025Review
- GPTAnno: Ontology-tree-guided hierarchical cell type annotation based on GPT models for single-cell data.bioRxiv : the preprint server for biology · 2025Article
- scExtract: leveraging large language models for fully automated single-cell RNA-seq data annotation and prior-informed multi-dataset integration.Genome biology · 2025Article
- Advancing automated cell type annotation with large language models and single-cell isoform sequencing.Computational and structural biotechnology journal · 2025Review
- Celline: a flexible tool for one-step retrieval and integrative analysis of public single-cell RNA sequencing data.Frontiers in bioinformatics · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Single-cell RNA sequencing has revolutionized cellular heterogeneity research, but analyzing the abundance of unannotated public datasets remains challenging. We present scExtract, a framework leveraging large language models to automate scRNA-seq data analysis from preprocessing to annotation and integration. scExtract extracts information from research articles to guide data processing, outperforming existing reference transfer methods in benchmarks. We introduce scanorama-prior and cellhint-prior, which incorporate prior annotation information for improved batch correction while preserving biological diversities. We demonstrate scExtract's utility by integrating 14 datasets to create a comprehensive human skin atlas of 440,000 cells.
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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.