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A new artificial intelligence (AI) model was able to detect 13 different types of cancer with 98.2% accuracy using only DNA data from tissue samples, according to a new study. The AI model, dubbed EMethylNET, was developed by researchers at the University of Cambridge in the U.K. and could potentially accelerate early cancer detection, diagnosis, and treatment. The findings, published last week in Biology Methods and Protocols, focused on DNA methylation, a chemical process that occurs early on when cells start to grow—including cancer cells. The researchers trained the machine learning model to spot early-building cancer structures and pathways. “Cancer, a collection of more than two hundred different diseases, remains a leading cause of morbidity and mortality worldwide,” the study noted. “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,” it continued. The researchers trained EMethylNET on data from more than 6,000 tissue samples from The Cancer Genome Atlas, representing 13 cancer types including breast, lung, and colorectal cancers. They then tested it on more than 900 samples from independent datasets. The top-line finding was more than 98% accuracy in classifying 13 cancer types and non-cancer samples. The study also highlighted the fact that the method performed well across diverse datasets from different countries. Researchers were also able to identify 3,388 methylation sites linked to cancer-related genes and pathways. According to the study, the AI model combines two AI approaches: XGBoost, which selects relevant features, and a deep neural network for classification. This allows it to not only accurately detect cancer, but also provide insights into the body’s regulation of non-genetic factors that mutate normal cells into cancer cells.  “These epigenetic modifications…

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Filed under: News - @ June 27, 2024 11:28 am