ArticleBiology methods & protocols2024
Early detection and diagnosis of cancer with interpretable machine learning to uncover cancer-specific DNA methylation patterns.
Article in Biology methods & protocols, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Who cites it
5 citing papers in PubMed.
- Article
- Discovery and validation of a novel dual-target blood test for the detection of hepatocellular carcinoma across stages from cirrhosis.BMC medicine · 2025Article
- Unveiling Pharmacogenomics Insights into Circular RNAs: Toward Precision Medicine in Cancer Therapy.Biomolecules · 2025Review
- Overview and current status of published research on cancer, sarcopenia and physical activity: A bibliometric analysis.AIMS public health · 2025Review
- Advancing epigenetic profiling in cervical cancer: machine learning techniques for classifying DNA methylation patterns.3 Biotech · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer, a collection of more than two hundred different diseases, remains a leading cause of morbidity and mortality worldwide. Usually detected at the advanced stages of disease, metastatic cancer accounts for 90% of cancer-associated deaths. Therefore, the early detection of cancer, combined with current therapies, would have a significant impact on survival and treatment of various cancer types. Epigenetic changes such as DNA methylation are some of the early events underlying carcinogenesis. Here, we report on an interpretable machine learning model that can classify 13 cancer types as well as non-cancer tissue samples using only DNA methylome data, with 98.2% accuracy. We utilize the features identified by this model to develop EMethylNET, a robust model consisting of an XGBoost model that provides information to a deep neural network that can generalize to independent data sets. We also demonstrate that the methylation-associated genomic loci detected by the classifier are associated with genes, pathways and networks involved in cancer, providing insights into the epigenomic regulation of carcinogenesis.
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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.