A recent study suggests that doctors may soon be able to utilize artificial intelligence to detect and diagnose cancer in patients, enabling earlier treatment.
Each cell in fact exhibits millions of these methyl groups. Thus, distinguishing noncancerous and cancerous tissue becomes the challenging task at hand especially with the desired speed. Researchers from the University of Cambridge and the Imperial College of London turned to fast computation technology. They observed changes in these DNA marks inThey worked with four different model types but presented the results from two that they used to create EMethylNET. It consists of a “DNN model trained on features learned from multiclass XGBoost.”
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