ArticlePloS one2020
Identifying transcriptomic correlates of histology using deep learning.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- The Evolution of Spatial Omics Technologies Introduces A Novel Avenue for Lung Cancer Research.Genomics, proteomics & bioinformatics · 2026Review
- Advances in predicting omics profiles from imaging data.Briefings in bioinformatics · 2026Review
- Generating crossmodal gene expression from cancer histopathology improves multimodal AI predictions.Nature communications · 2025Article
- Machine-learning models based on histological images from healthy donors identify imageQTLs and predict chronological age.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Img2ST-Net: efficient high-resolution spatial omics prediction from whole-slide histology images via fully convolutional image-to-image learning.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Machine Learning Models Based on Histological Images from Healthy Donors Identify ImageQTLs and Predict Chronological Age.bioRxiv : the preprint server for biology · 2025Article
- Building digital histology models of transcriptional tumor programs with generative deep learning for pathology-based precision medicine.Genome medicine · 2025Article
- A novel multislice framework for precision 3D spatial domain reconstruction and disease pathology analysis.Genome research · 2025Article
- Demystifying the black box: A survey on explainable artificial intelligence (XAI) in bioinformatics.Computational and structural biotechnology journal · 2025Review
- Inferring super-resolution tissue architecture by integrating spatial transcriptomics with histology.Nature biotechnology · 2024Article
- Article
- The landscape of expression and alternative splicing variation across human traits.Cell genomics · 2023Article
- Automated image analysis method to detect and quantify fat cell infiltration in hematoxylin and eosin stained human pancreas histology images.BBA advances · 2023Article
- Self-supervised learning of cell type specificity from immunohistochemical images.Bioinformatics (Oxford, England) · 2022Article
- Virtual Gene Concept and a Corresponding Pragmatic Research Program in Genetical Data Science.Entropy (Basel, Switzerland) · 2021Review
- Integrating pathomics with radiomics and genomics for cancer prognosis: A brief review.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2021Article
- The impact of site-specific digital histology signatures on deep learning model accuracy and bias.Nature communications · 2021Article
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2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Linking phenotypes to specific gene expression profiles is an extremely important problem in biology, which has been approached mainly by correlation methods or, more fundamentally, by studying the effects of gene perturbations. However, genome-wide perturbations involve extensive experimental efforts, which may be prohibitive for certain organisms. On the other hand, the characterization of the various phenotypes frequently requires an expert's subjective interpretation, such as a histopathologist's description of tissue slide images in terms of complex visual features (e.g. 'acinar structures'). In this paper, we use Deep Learning to eliminate the inherent subjective nature of these visual histological features and link them to genomic data, thus establishing a more precisely quantifiable correlation between transcriptomes and phenotypes. Using a dataset of whole slide images with matching gene expression data from 39 normal tissue types, we first developed a Deep Learning tissue classifier with an accuracy of 94%. Then we searched for genes whose expression correlates with features inferred by the classifier and demonstrate that Deep Learning can automatically derive visual (phenotypical) features that are well correlated with the transcriptome and therefore biologically interpretable. As we are particularly concerned with interpretability and explainability of the inferred histological models, we also develop visualizations of the inferred features and compare them with gene expression patterns determined by immunohistochemistry. This can be viewed as a first step toward bridging the gap between the level of genes and the cellular organization of tissues.
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