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Early detection and diagnosis of cancer with interpretable machine learning to uncover cancer-specific DNA methylation patterns

Biol Methods Protoc, Vol. 9, Issue 1, Article Number: bpae028, 2024

PMID: 38903861

In this study, the authors present an interpretable machine learning model that can classify 13 cancer types and non-cancer tissue samples using only DNA methylome data, achieving 98.2% accuracy. The features identified by this model are utilized to develop EMethylNET, a robust framework consisting of an XGBoost model that informs a deep neural network, capable of generalizing to independent data sets. The authors also demonstrate that the methylation-associated genomic loci detected by the classifier are linked to genes, pathways, and networks involved in cancer, providing insights into the epigenomic regulation of carcinogenesis.

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