ArticleNature biomedical engineering2025
Deep profiling of gene expression across 18 human cancers.
Article in Nature biomedical engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- MoESurv: a zero-sample and transferable survival prediction framework for rare cancers using mixture of experts.Bioinformatics (Oxford, England) · 2026Article
- Data-driven RNA phenotyping captures genetically regulated dimensions of the transcriptome.American journal of human genetics · 2026Article
- Advancing AI for multi-omics and clinical data integration in basic and translational cancer research.Nature reviews. Cancer · 2026Review
- Article
- Integrating single-cell and single-nucleus datasets improves bulk RNA-seq deconvolution.Cell reports methods · 2026Article
- Rapid Decentralized Prostate Cancer Risk Stratification by Portable Liquid Biopsy Analysis within a Clinical Biosensor Validation Framework.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Data-driven RNA phenotyping captures genetically regulated dimensions of the transcriptome.bioRxiv : the preprint server for biology · 2026Article
- ULSL: Unified Latent and Similarity Learning for robust multi-omics cancer subtype identification.Bioinformatics advances · 2026Article
- A systematic evaluation of explainable AI methods for high-dimensional transcriptome-based cancer survival prediction.Frontiers in physiology · 2026Article
- Pan-cancer analysis shapes the understanding of cancer biology and medicine.Cancer communications (London, England) · 2025Review
- Can AI reveal the next generation of high-impact bone genomics targets?Bone reports · 2025Review
- Latent spaces for tumour transcriptomes.Nature biomedical engineering · 2025Article
- CCL4L2 is a potential biomarker for differentiating central and peripheral vertigo.Frontiers in integrative neuroscience · 2025Article
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Authors and funding
7 authors.
Funding
Abstract
Clinical and biological information in large datasets of gene expression across cancers could be tapped with unsupervised deep learning. However, difficulties associated with biological interpretability and methodological robustness have made this impractical. Here we describe an unsupervised deep-learning framework for the generation of low-dimensional latent spaces for gene-expression data from 50,211 transcriptomes across 18 human cancers. The framework, which we named DeepProfile, outperformed dimensionality-reduction methods with respect to biological interpretability and allowed us to unveil that genes that are universally important in defining latent spaces across cancer types control immune cell activation, whereas cancer-type-specific genes and pathways define molecular disease subtypes. By linking latent variables in DeepProfile to secondary characteristics of tumours, we discovered that mutation burden is closely associated with the expression of cell-cycle-related genes, and that the activity of biological pathways for DNA-mismatch repair and MHC class II antigen presentation are consistently associated with patient survival. We also found that tumour-associated macrophages are a source of survival-correlated MHC class II transcripts. Unsupervised learning can facilitate the discovery of biological insight from gene-expression data.
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39690287What Socratic holds
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.