ArticleNature methods2025
A visual-omics foundation model to bridge histopathology with spatial transcriptomics.
Article in Nature methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers.
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Who cites it
70 citing papers in PubMed.
- 3D multi-omics tumour atlases: from technology to biology and clinical translation.Nature reviews. Cancer · 2026Review
- Advancing the Deciphering of Host-Microbe Crosstalk with Spatial Omics: A Mini-Review.Current microbiology · 2026Review
- Deep Learning-Assisted Prioritization of Candidate Drug Targets in Tumors Using Spatial Multi-Omics.Biology · 2026Review
- Histological aging signatures for monitoring tissue-specific aging and disease.Nature medicine · 2026Article
- From descriptive to generative: foundation-model approaches for spatial transcriptomics.Briefings in bioinformatics · 2026Review
- ST-ConMa: a multimodal foundation framework for spatial transcriptomics via image-gene contrastive and matching learning.Briefings in bioinformatics · 2026Article
- Unified representation learning for spatial multi-omics.Bioinformatics (Oxford, England) · 2026Article
- ConMIL: interactive and contrastive text-guided multiple instance learning for whole slide image classification.Bioinformatics (Oxford, England) · 2026Article
- Review
- Multi-omics-driven precision medicine.iMeta · 2026Review
- Spatial omics illuminates tumor heterogeneity.Cell reports methods · 2026Review
- Genome-scale molecular profiling from routine histopathology with a multimodal pathology foundation model.Research square · 2026Article
- Spatially resolved tissue architecture and computational pathology in pancreatic cancer.Experimental & molecular medicine · 2026Review
- Leveraging multi-modal foundation models for analysing spatial multi-omic and histopathology data.Nature biomedical engineering · 2026Article
- Tracing the rise of biomedical foundation models.Nature biotechnology · 2026Article
- Deep-learning-enabled multi-omics analyses for prediction of future metastasis in cancer.Nature communications · 2026Article
- Multimodal spatial omics: From data acquisition to computational integration.Patterns (New York, N.Y.) · 2026Review
- Translating genome-wide association studies at multiple scales: Drug target prioritization, cellular architectures, and organ imaging.Cell genomics · 2026Review
- Imaging-anchored multiomics in cardiovascular disease: integrating cardiac imaging, bulk, single-cell, and spatial transcriptomics.Briefings in bioinformatics · 2026Review
- Histopathology-centered computational evolution of spatial omics: integration, mapping, and foundation models.Briefings in bioinformatics · 2026Review
10 more citing papers are in PubMed but not listed here.
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Authors and funding
20 authors.
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Abstract
Artificial intelligence has revolutionized computational biology. Recent developments in omics technologies, including single-cell RNA sequencing and spatial transcriptomics, provide detailed genomic data alongside tissue histology. However, current computational models focus on either omics or image analysis, lacking their integration. To address this, we developed OmiCLIP, a visual-omics foundation model linking hematoxylin and eosin images and transcriptomics using tissue patches from Visium data. We transformed transcriptomic data into 'sentences' by concatenating top-expressed gene symbols from each patch. We curated a dataset of 2.2 million paired tissue images and transcriptomic data across 32 organs to train OmiCLIP integrating histology and transcriptomics. Building on OmiCLIP, our Loki platform offers five key functions: tissue alignment, annotation via bulk RNA sequencing or marker genes, cell-type decomposition, image-transcriptomics retrieval and spatial transcriptomics gene expression prediction from hematoxylin and eosin-stained images. Compared with 22 state-of-the-art models on 5 simulations, and 19 public and 4 in-house experimental datasets, Loki demonstrated consistent accuracy and robustness.
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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.