Boffins build AI to identify genetic mutations • The Register

Machine learning techniques, such as deep learning, have proven surprisingly effective at identifying diseases like breast cancer. However, when it comes to identifying mutations at the genetic level, these models have come up short, according to researchers at the University of California San Diego (UCSD). In a paper published in the journal Nature Biotechnology this week, researchers at the university propose a new machine learning framework called DeepMosaic that uses a combination of image-based visualization and deep learning models to identify genetic mutations associated with diseases including cancer and disorders with genetic links, such as autism spectrum disorder. Using AI/ML to identify disease has been a hot topic in recent years. The problem, according to UCSD professor Joe Gleeson, is most of these models aren't well suited to identifying genetic mutations, called mosaic variants or mutations, because most of the software developed over the last two decades was trained on cancer samples. Because cancer cells divide so rapidly, they're relatively easy to spot for computer programs, he explained in an interview with The Register.