ArticleNature communications2024
Digital profiling of gene expression from histology images with linearized attention.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 2 of them syntheses that pooled it.
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Who cites it
45 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Transforming histologic assessment: artificial intelligence in cancer diagnosis and personalized treatment.British journal of cancer · 2025Pooled it
- A systematic review on the generative AI applications in human medical genetics.Frontiers in genetics · 2025Pooled it
- Molecular histomorphometry and the emerging role of transcriptomics in celiac disease diagnosis and therapeutic trials.Annals of medicine · 2026Review
- From descriptive to generative: foundation-model approaches for spatial transcriptomics.Briefings in bioinformatics · 2026Review
- DeepPathway: predicting pathway expression from histopathology images.Bioinformatics (Oxford, England) · 2026Article
- Epithelial-mesenchymal transition in carcinoma: navigating phenotypic states to target resistance and metastasis.Cancer metastasis reviews · 2026Review
- ALPaCA: Adapting Llama for Pathology Context Analysis to enable slide-level question answering.Nature communications · 2026Article
- A benchmark study of vision and pathology foundation models for computational pathology.Nature communications · 2026Article
- Human genetics across levels of biological organization.Nature reviews. Genetics · 2026Review
- Artificial intelligence virtual extracellular vesicles (AIVEVs).Bioactive materials · 2026Review
- Deep learning-based prediction of gene expression from histopathology identifies NR5A1 as a candidate biomarker and druggable target in high-grade serous ovarian carcinoma.Journal of ovarian research · 2026Article
- Weak supervision of H&E slides reveals systems-level biology and functional states that govern therapeutic resistance.bioRxiv : the preprint server for biology · 2026Article
- From histology to spatial transcriptomics: establishing a lightweight single-patch baseline.BMC bioinformatics · 2026Article
- Histology-Derived Signatures Predict Recurrence Risk and Chemotherapy Benefit in Randomized Trials of Early Breast Cancer.medRxiv : the preprint server for health sciences · 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
- Predicting protein cascade expression from H&E images.PLoS computational biology · 2026Article
- Defining the tumor microenvironment of non-small cell lung cancer.Immunology and cell biology · 2026Review
- Deep learning inference of cell type-specific gene expression from breast tumor histopathology.NPJ precision oncology · 2026Article
- Is cancer the result of uncontrolled cellular growth? a glance into the tumorigenic process.Cancer cell international · 2026Review
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9 authors.
Funding
Abstract
Cancer is a heterogeneous disease requiring costly genetic profiling for better understanding and management. Recent advances in deep learning have enabled cost-effective predictions of genetic alterations from whole slide images (WSIs). While transformers have driven significant progress in non-medical domains, their application to WSIs lags behind due to high model complexity and limited dataset sizes. Here, we introduce SEQUOIA, a linearized transformer model that predicts cancer transcriptomic profiles from WSIs. SEQUOIA is developed using 7584 tumor samples across 16 cancer types, with its generalization capacity validated on two independent cohorts comprising 1368 tumors. Accurately predicted genes are associated with key cancer processes, including inflammatory response, cell cycles and metabolism. Further, we demonstrate the value of SEQUOIA in stratifying the risk of breast cancer recurrence and in resolving spatial gene expression at loco-regional levels. SEQUOIA hence deciphers clinically relevant information from WSIs, opening avenues for personalized cancer management.
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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 Socratic graph.