AI Analyzes cfDNA Ends from Liquid Biopsies to Detect Cancer
There may be new hope in the hunt for reliable ways to detect cancer early on, an area where traditional diagnostic methods often prove inadequate. By analyzing cell-free DNA end-motifs, AI can now distinguish between cancer patients and healthy individuals, according to a research article published in npj Precision Oncology. A model based on deep learning called end-motif inspection via transformer (EMIT) was created using thousands of samples from various studies that included cfDNA sequencing for hepatocellular carcinoma (HCC), colorectal cancer (CRC), non-small cell lung cancer (NSCLC), and esophageal carcinoma (ESCA). Tested on whole exome sequencing data from both lung cancer and non-cancer patients, EMIT demonstrated strong classification capabilities. Developed by researchers at Tianjin Medical University, EMIT is an advance toward a standardized deep-learning method for identifying cfDNA fragmentome end-motifs. Using cfDNA for cancer diagnosis Linear cfDNA fragments, which ex...