ArticleCommunications biology2023
Learning to predict RNA sequence expressions from whole slide images with applications for search and classification.
Article in Communications biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Multi-Scale Mapping of Gene Expression from Whole-slide Images for Identifying Phenotype-Associated Subpopulations.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Deep learning inference of cell type-specific gene expression from breast tumor histopathology.NPJ precision oncology · 2026Article
- From classical machine learning to emerging foundation models: review on multimodal data integration for cancer research.Artificial intelligence review · 2026Article
- Advances in predicting omics profiles from imaging data.Briefings in bioinformatics · 2026Review
- Path2Omics Enhances Transcriptomic and Methylation Prediction Accuracy from Tumor Histopathology.Cancer research · 2025Article
- Reproducibility of digital pathology features extracted from deep learning and foundational AI models on sequential tissue slides.Scientific reports · 2025Article
- Advancing genome-based precision medicine: a review on machine learning applications for rare genetic disorders.Briefings in bioinformatics · 2025Review
- Spatial histology and gene-expression representation and generative learning via online self-distillation contrastive learning.Briefings in bioinformatics · 2025Article
- Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis.Genomics, proteomics & bioinformatics · 2025Review
- Unravelling tumour spatiotemporal heterogeneity using spatial multimodal data.Clinical and translational medicine · 2025Review
- Efficient merging and validation of deep learning-based nuclei segmentations in H&E slides from multiple models.Journal of pathology informatics · 2025Article
- Histopathology based AI model predicts anti-angiogenic therapy response in renal cancer clinical trial.Nature communications · 2025Article
- Multimodal learning for scalable representation of high-dimensional medical data.Frontiers in digital health · 2025Article
- On image search in histopathology.Journal of pathology informatics · 2024Review
- Digital profiling of gene expression from histology images with linearized attention.Nature communications · 2024Article
- A deep-learning framework to predict cancer treatment response from histopathology images through imputed transcriptomics.Nature cancer · 2024Article
- MOSBY enables multi-omic inference and spatial biomarker discovery from whole slide images.Scientific reports · 2024Article
- AI-driven Discovery of Morphomolecular Signatures in Toxicology.bioRxiv : the preprint server for biology · 2024Article
- Histopathology Based AI Model Predicts Anti-Angiogenic Therapy Response in Renal Cancer Clinical Trial.ArXiv · 2024Article
- Surrogate Biomarker Prediction from Whole-Slide Images for Evaluating Overall Survival in Lung Adenocarcinoma.Diagnostics (Basel, Switzerland) · 2024Article
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5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning methods are widely applied in digital pathology to address clinical challenges such as prognosis and diagnosis. As one of the most recent applications, deep models have also been used to extract molecular features from whole slide images. Although molecular tests carry rich information, they are often expensive, time-consuming, and require additional tissue to sample. In this paper, we propose tRNAsformer, an attention-based topology that can learn both to predict the bulk RNA-seq from an image and represent the whole slide image of a glass slide simultaneously. The tRNAsformer uses multiple instance learning to solve a weakly supervised problem while the pixel-level annotation is not available for an image. We conducted several experiments and achieved better performance and faster convergence in comparison to the state-of-the-art algorithms. The proposed tRNAsformer can assist as a computational pathology tool to facilitate a new generation of search and classification methods by combining the tissue morphology and the molecular fingerprint of the biopsy samples.
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