ArticleEBioMedicine2021
Deep learning predicts gene expression as an intermediate data modality to identify susceptibility patterns in Mycobacterium tuberculosis infected Diversity Outbred mice.
Article in EBioMedicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed, 39 citations in OpenAlex.
- Drug-associated cytokine release syndrome: a FAERS pharmacovigilance study with complementary transcriptomic analysis.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- GenoGlyph: Pan-cancer genomic mutation inference and risk stratification from diagnostic histopathology slides.Research square · 2026Article
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- Advances in predicting omics profiles from imaging data.Briefings in bioinformatics · 2026Review
- Fifteen years of the Diversity Outbred mouse model: a review.Mammalian genome : official journal of the International Mammalian Genome Society · 2026Review
- Inferring Clinically Relevant Molecular Subtypes of Pancreatic Cancer from Routine Histopathology Using Deep Learning.Research square · 2026Article
- Inferring Clinically Relevant Molecular Subtypes of Pancreatic Cancer from Routine Histopathology Using Deep Learning.medRxiv : the preprint server for health sciences · 2026Article
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- Immunological roads diverged: mapping tuberculosis outcomes in mice.Trends in microbiology · 2025Review
- Cross-Attention-Based Saliency Inference for Predicting Cancer Metastasis on Whole Slide Images.IEEE journal of biomedical and health informatics · 2024Article
- Self-supervised learning for characterising histomorphological diversity and spatial RNA expression prediction across 23 human tissue types.Nature communications · 2024Article
- B cells in perivascular and peribronchiolar granuloma-associated lymphoid tissue and B-cell signatures identify asymptomaticInfection and immunity · 2024Article
- Systems genetics uncover new loci containing functional gene candidates in Mycobacterium tuberculosis-infected Diversity Outbred mice.PLoS pathogens · 2024Article
- Enhancing Colorectal Cancer Tumor Bud Detection Using Deep Learning from Routine H&E-Stained Slides.Proceedings of SPIE--the International Society for Optical Engineering · 2024Article
- Translating prognostic quantification of c-MYC and BCL2 from tissue microarrays to whole slide images in diffuse large B-cell lymphoma using deep learning.Diagnostic pathology · 2024Article
- The chosen few:Frontiers in immunology · 2024Review
- Prediction of Tuberculosis From Lung Tissue Images of Diversity Outbred Mice Using Jump Knowledge Based Cell Graph Neural Network.IEEE access : practical innovations, open solutions · 2024Article
- Systems genetics uncover new loci containing functional gene candidates inbioRxiv : the preprint server for biology · 2023Article
- DeeP4med: deep learning for P4 medicine to predict normal and cancer transcriptome in multiple human tissues.BMC bioinformatics · 2023Article
- Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundMachine learning sustains successful application to many diagnostic and prognostic problems in computational histopathology. Yet, few efforts have been made to model gene expression from histopathology. This study proposes a methodology which predicts selected gene expression values (microarray) from haematoxylin and eosin whole-slide images as an intermediate data modality to identify fulminant-like pulmonary tuberculosis ('supersusceptible') in an experimentally infected cohort of Diversity Outbred mice (n=77).
methodsGradient-boosted trees were utilized as a novel feature selector to identify gene transcripts predictive of fulminant-like pulmonary tuberculosis. A novel attention-based multiple instance learning model for regression was used to predict selected genes' expression from whole-slide images. Gene expression predictions were shown to be sufficiently replicated to identify supersusceptible mice using gradient-boosted trees trained on ground truth gene expression data.
findingsThe model was accurate, showing high positive correlations with ground truth gene expression on both cross-validation (n = 77, 0.63 ≤ ρ ≤ 0.84) and external testing sets (n = 33, 0.65 ≤ ρ ≤ 0.84). The sensitivity and specificity for gene expression predictions to identify supersusceptible mice (n=77) were 0.88 and 0.95, respectively, and for an external set of mice (n=33) 0.88 and 0.93, respectively. IMPLICATIONS: Our methodology maps histopathology to gene expression with sufficient accuracy to predict a clinical outcome. The proposed methodology exemplifies a computational template for gene expression panels, in which relatively inexpensive and widely available tissue histopathology may be mapped to specific genes' expression to serve as a diagnostic or prognostic tool.
fundingNational Institutes of Health and American Lung Association.
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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 OpenQuestion graph.