Researchers use machine learning to unlock the genomic code in clinical cancer samples

A new paper from University of Helsinki, published today in Nature Communications, suggests a method for accurately analyzing genomics data in cancer archival biopsies. This tool uses machine learning methods to correct damaged DNA and unveil the true mutation processes in tumor samples. This helps to unlock tremendous medicine values in millions of archival cancer samples.