ArticleNature cancer2025
crossNN is an explainable framework for cross-platform DNA methylation-based classification of tumors.
Article in Nature cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Clinical evaluation of the CrossNN DNA methylation classifier for central nervous system tumors.Brain pathology (Zurich, Switzerland) · 2026Article
- DNA methylation profiling enables subclassification of mucinous ovarian carcinoma and distinguishes it from extraovarian mucinous metastases.Cell reports. Medicine · 2026Article
- Machine learning-driven cancer diagnostics with improved robustness and interpretability.Chemical science · 2026Review
- Whole genome sequencing of pre-treatment and post-treatment locally advanced rectal cancer using long and short read technologies.Scientific reports · 2026Article
- A scalable Tn5-based method for genome-wide DNA methylation profiling in development and disease.Nature communications · 2026Article
- Integrative CSF profiling identifies disease-specific immune responses in leptomeningeal disease.Cell reports. Medicine · 2026Article
- AI-Based Methods in Neuropathology for Diagnosis and Treatment of Brain Tumors.European journal of neurology · 2026Review
- A MAGIBU-based model for pediatric and juvenile CNS tumors: an in-house epigenetic decision-support framework compared with online DNA methylation classifiers.Free neuropathology · 2026Article
- Advancements in DNA methylation technologies and their application in cancer diagnosis.Epigenetics · 2025Review
- Epigenetic reprogramming as the nexus of cancer stemness and therapy resistance: implications for biomarker discovery.Discover oncology · 2025Review
- Employing nanopore sequencing on FFPE-derived DNA for CNS tumor diagnostics.Acta neuropathologica communications · 2025Article
- DNA methylation and machine learning: challenges and perspective toward enhanced clinical diagnostics.Clinical epigenetics · 2025Review
- ROBIN: A unified nanopore-based assay integrating intraoperative methylome classification and next-day comprehensive profiling for ultra-rapid tumor diagnosis.Neuro-oncology · 2025Article
- Interpretable Multi-Cancer Early Detection Using SHAP-Based Machine Learning on Tumor-Educated Platelet RNA.Diagnostics (Basel, Switzerland) · 2025Article
- Epigenetic crosstalk between stem cells and tumors: mechanisms and emerging perspectives.American journal of stem cells · 2025Review
- Detection of diagnostic somatic copy number alterations from cerebrospinal fluid cell-free DNA in brain tumor patients.Acta neuropathologica communications · 2024Article
- Rapid DNA methylation-based classification of pediatric brain tumors from ultrasonic aspirate specimens.Journal of neuro-oncology · 2024Article
- Dataset of DNA methylation profiles of 189 pediatric central nervous system, soft tissue, and bone tumors.Data in brief · 2024Article
Corrections and comments
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Authors and funding
26 authors.
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Abstract
DNA methylation-based classification of (brain) tumors has emerged as a powerful and indispensable diagnostic technique. Initial implementations used methylation microarrays for data generation, while most current classifiers rely on a fixed methylation feature space. This makes them incompatible with other platforms, especially different flavors of DNA sequencing. Here, we describe crossNN, a neural network-based machine learning framework that can accurately classify tumors using sparse methylomes obtained on different platforms and with different epigenome coverage and sequencing depth. It outperforms other deep and conventional machine learning models regarding accuracy and computational requirements while still being explainable. We use crossNN to train a pan-cancer classifier that can discriminate more than 170 tumor types across all organ sites. Validation in more than 5,000 tumors profiled on different platforms, including nanopore and targeted bisulfite sequencing, demonstrates its robustness and scalability with 99.1% and 97.8% precision for the brain tumor and pan-cancer models, respectively.
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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.