ArticleCancer cell2025
Classification of non-TCGA cancer samples to TCGA molecular subtypes using compact feature sets.
Article in Cancer cell, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
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The trial behind it
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Who cites it
21 citing papers in PubMed.
- Contextual evaluation of microRNA sequencing data harmonization for sample clustering.Briefings in bioinformatics · 2026Article
- Article
- A Generalizable and Interpretable Framework for Molecular Subtype Classification of Pancreatic Ductal Adenocarcinoma Integrating Conformal Uncertainty Quantification and Consensus-Based Explainable Artificial Intelligence Across Multiple Cohorts.International journal of molecular sciences · 2026Article
- Single-cell and bulk transcriptomics identify senescence-related EMT transcriptional programs and a prognostic framework in pancreatic ductal adenocarcinoma.BMC gastroenterology · 2026Article
- Advancing AI for multi-omics and clinical data integration in basic and translational cancer research.Nature reviews. Cancer · 2026Review
- POTEF upregulation drives hepatocellular carcinoma progression via oncogenic signaling and immune modulation.Scientific reports · 2026Article
- Prognostic significance and pathological correlation analysis of DKK4 in colorectal cancer.Translational cancer research · 2026Article
- Artificial intelligence for precision oncology from phenotyping and drug discovery to clinical translation.Discover oncology · 2026Review
- KRT16 and APOA1: key regulators of proliferation and lipid metabolism in non-small cell lung cancer.Open life sciences · 2026Article
- Pooling multimodal cancer data across unaligned embedding spaces maintains tumor of origin signal.Bioinformatics advances · 2026Article
- LINC00460 drives clear cell renal cell carcinoma progression via complement/coagulation and p53 pathways: a potential therapeutic target.American journal of translational research · 2026Article
- The application of AI-driven and engineered intratumoral microbes in cancer therapy.Journal of translational medicine · 2025Review
- Transient Receptor Potential Melastatin 4 (TRPM4) is associated with an immuno-stimulatory TME and better prognosis in BLCA.Discover oncology · 2025Article
- Precision Oncology: Current Landscape, Emerging Trends, Challenges, and Future Perspectives.Cells · 2025Review
- Shaping Precision Medicine: The Journey of Sequencing Technologies Across Human Solid Tumors.Biomedicines · 2025Review
- Multidimensional decoding of colorectal cancer heterogeneity: Artificial intelligence-enabled precision exploration of single-cell and spatial transcriptomics.World journal of gastrointestinal oncology · 2025Review
- Decoding meningioma prognosis with multi-omics: macrophage diversity, immune-CNV interplay, and novel SPP1-targeted strategies.Journal of neuro-oncology · 2025Article
- HallmarkGraph: a cancer hallmark informed graph neural network for classifying hierarchical tumor subtypes.Bioinformatics (Oxford, England) · 2025Article
- APOBEC3C-Mediated NF-κB Activation Promotes Malignant Progression of Gliomas.Immunity, inflammation and disease · 2025Article
- Molecular basis and therapeutic implications of binary YAPOn/YAPOff cancer classes.The Biochemical journal · 2025Review
Corrections and comments
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Authors and funding
30 authors.
Funding
Abstract
Molecular subtypes, such as defined by The Cancer Genome Atlas (TCGA), delineate a cancer's underlying biology, bringing hope to inform a patient's prognosis and treatment plan. However, most approaches used in the discovery of subtypes are not suitable for assigning subtype labels to new cancer specimens from other studies or clinical trials. Here, we address this barrier by applying five different machine learning approaches to multi-omic data from 8,791 TCGA tumor samples comprising 106 subtypes from 26 different cancer cohorts to build models based upon small numbers of features that can classify new samples into previously defined TCGA molecular subtypes-a step toward molecular subtype application in the clinic. We validate select classifiers using external datasets. Predictive performance and classifier-selected features yield insight into the different machine-learning approaches and genomic data platforms. For each cancer and data type we provide containerized versions of the top-performing models as a public resource.
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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.