ArticleNature communications2025
Benchmarking the translational potential of spatial gene expression prediction from histology.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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Who cites it
41 citing papers in PubMed.
- Decoding epithelial-mesenchymal transitions with multi-omics.Nature reviews. Genetics · 2026Review
- Deep Learning-Assisted Prioritization of Candidate Drug Targets in Tumors Using Spatial Multi-Omics.Biology · 2026Review
- Spatially guided translation from histology images to transcriptomic profiles using foundation model-driven contrastive learning.PLoS computational biology · 2026Article
- DeepPathway: predicting pathway expression from histopathology images.Bioinformatics (Oxford, England) · 2026Article
- DFCE-KanT: predicting spatial gene expression from histology images via contrastive learning.Bioinformatics (Oxford, England) · 2026Article
- Prediction of single cell expression from low-plex immunofluorescence images for guiding precision oncology.NPJ precision oncology · 2026Article
- Trimodal, uncertainty-guided whole-slide framework for genome-scale spatial expression and image-only virtual perturbation in cancer cohorts.Genome medicine · 2026Article
- 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
- Computational analysis in spatial transcriptomics: methods and perspectives.Briefings in bioinformatics · 2026Review
- Imaging-anchored multiomics in cardiovascular disease: integrating cardiac imaging, bulk, single-cell, and spatial transcriptomics.Briefings in bioinformatics · 2026Review
- Review
- CPS: mapping physical coordinates to high-fidelity spatial transcriptomics via privileged multi-scale context distillation.Bioinformatics (Oxford, England) · 2026Article
- privateST: a feasible framework for privacy-preserving spatial transcriptomics prediction from histopathology images.Scientific reports · 2026Article
- A comprehensive survey of computer vision methods for spatial transcriptomics.Briefings in bioinformatics · 2026Review
- Multi-Scale Mapping of Gene Expression from Whole-slide Images for Identifying Phenotype-Associated Subpopulations.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- SensX: Model-Agnostic Local Feature Attribution via Calibrated Global Sensitivity Analysis.Transactions on machine learning research · 2026Article
- AI and the digital pathology revolution: clinical applications in cancer diagnosis and assessment.Expert review of molecular diagnostics · 2026Review
- Spatial transcriptomics and artificial intelligence: a scoping review of emerging applications in head and neck pathology.Head and neck pathology · 2026Article
- HiST: Histological Images Reconstruct Tumor Spatial Transcriptomics via MultiScale Fusion Deep Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
Corrections and comments
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7 authors.
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Abstract
Spatial transcriptomics has enabled the quantification of gene expression at spatial coordinates across a tissue, offering crucial insights into molecular underpinnings of diseases. In light of this, several methods predicting spatial gene expression from paired histology images have provided the opportunity to enhance the utility of obtainable and cost-effective haematoxylin-and-eosin-stained histology images. To this end, we conduct a comprehensive benchmarking study encompassing eleven methods for predicting spatial gene expression with histology images. These methods are reproduced and evaluated using five Spatially Resolved Transcriptomics datasets, followed by external validation using The Cancer Genome Atlas data. Our evaluation incorporates diverse metrics which capture the performance of predicted gene expression, model generalisability, translational potential, usability and computational efficiency of each method. Our findings demonstrate the capacity of the methods to predict spatial gene expression from histology and highlight areas that can be addressed to support the advancement of this emerging field.
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