ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026
CELLama: Foundation Model for Single Cell and Spatial Transcriptomics by Cell Embedding Leveraging Language Model Abilities.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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
11 citing papers in PubMed.
- From descriptive to generative: foundation-model approaches for spatial transcriptomics.Briefings in bioinformatics · 2026Review
- Multi-omics-driven precision medicine.iMeta · 2026Review
- Histopathology-centered computational evolution of spatial omics: integration, mapping, and foundation models.Briefings in bioinformatics · 2026Review
- Single-Cell and Spatial Omics: Methods and Applications.MedComm · 2026Review
- CELLama: Foundation Model for Single Cell and Spatial Transcriptomics by Cell Embedding Leveraging Language Model Abilities.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A unified framework enables accessible deployment and comprehensive benchmarking of single-cell foundation models.bioRxiv : the preprint server for biology · 2026Article
- Omics-based large language models: A new engine for drug discovery innovation.Acta pharmaceutica Sinica. B · 2026Review
- Decrypting cancer's spatial code: from single cells to tissue niches.Molecular oncology · 2025Review
- Foundation models in bioinformatics.National science review · 2025Review
- Primer on machine learning applications in brain immunology.Frontiers in bioinformatics · 2025Review
- Machine learning to dissect perturbations in complex cellular systems.Computational and structural biotechnology journal · 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
8 authors.
Funding
Abstract
Large-scale single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) have transformed biomedical research into a data-driven field, enabling the creation of comprehensive atlases. These methodologies facilitate detailed understanding of biology and pathophysiology; however, the complexity and sheer volume of data present analytical challenges, particularly in robust cell typing, integration, and understanding complex spatial relationships of cells. To address these challenges, CELLama (Cell Embedding Leverage Language Model Abilities) develops a framework that leverage language model to transform cell data into "sentences" that encapsulate gene expressions and metadata, enabling universal cell embedding. CELLama, serving as a foundation model, supports flexible applications ranging from cell typing to analysis of spatial contexts, independent of complex dataset-specific analysis workflows by using a large cell atlas. The results demonstrate that CELLama has significant potential to transform cellular analysis in various contexts, from determining cell types using multi-tissue atlases and their interactions to unraveling intricate tissue dynamics.
Indexed as
Identifiers
What 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.