ArticleNature biomedical engineering2026
Leveraging multi-modal foundation models for analysing spatial multi-omic and histopathology data.
Article in Nature biomedical engineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Review
- Translating genome-wide association studies at multiple scales: Drug target prioritization, cellular architectures, and organ imaging.Cell genomics · 2026Review
- Histopathology-centered computational evolution of spatial omics: integration, mapping, and foundation models.Briefings in bioinformatics · 2026Review
- A comprehensive survey of computer vision methods for spatial transcriptomics.Briefings in bioinformatics · 2026Review
- The neuroimmune axis in breast cancer: from mechanistic insights to clinical applications.BMC medicine · 2026Review
- A review on in-silico analysis of immune cell trafficking and interactions with the tumour microenvironment.Frontiers in oncology · 2026Review
- Instability of LLM text embeddings for unsupervised dimension reduction of tabular data.Frontiers in bioinformatics · 2026Article
- Medical imaging in immunotherapy response evaluation: from RECIST to AI-driven longitudinal models, and the distinction between risk prediction and kinetic diagnosis.Frontiers in medicine · 2026Review
- Multimodal AI in high-grade serous ovarian cancer: integrated prediction and clinical decision-making.Frontiers in oncology · 2026Review
- Decoding cell fate: integrated experimental and computational analysis at the single-cell level.Bioinformatics (Oxford, England) · 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
12 authors.
Funding
Abstract
Recent advances in pathology foundation models, pre-trained on large-scale histopathology images, have greatly advanced disease-focused applications. At the same time, spatial multi-omic technologies now measure gene and protein expression with high spatial resolution, offering valuable insights into tissue context. Yet, existing models struggle to integrate these complementary data types. Here, to address this challenge, we present spEMO, a computational framework that unifies embeddings from pathology foundation models and large language models for spatial multi-omic analysis. By leveraging multi-modal representations, spEMO surpasses single-modality models across diverse downstream tasks, including spatial domain identification, spot-type classification, whole-slide disease prediction and interpretation, multicellular interaction inference and automated medical reporting. These results highlight spEMO's strength in both biological discovery and clinical applications. Furthermore, we introduce a new benchmark task-multi-modal alignment-to evaluate how effectively pathology foundation models retrieve complementary information. Together, spEMO establishes a powerful step towards holistic, interpretable and generalizable AI for spatial biology and pathology.
Indexed as
Identifiers
41644824What 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.