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.

Scientists from the University of California San Diego School of Medicine and Rady Children's Institute for Genomic Medicine have developed a method for identifying mosaic mutations using deep learning. The process involves training a model to analyze large amounts of genomic data and recognize patterns associated with mosaic mutations. The researchers hope that this approach will help increase our understanding of the genetic basis of disease and lead to the development of more effective treatments. Genetic mutations can lead to a wide range of disorders that are often difficult to treat or understand. One type of mutation, called mosaic mutations, is particularly challenging to identify because it only affects a small percentage of cells.

Genetic mutations cause hundreds of unsolved and untreatable disorders. Among them, DNA mutations in a small percentage of cells, called mosaic mutations, are extremely difficult to detect because they exist in a tiny percentage of the cells. Current DNA mutation software detectors, while scanning the 3 billion bases of the human genome, are not well suited to discern mosaic mutations hiding among normal DNA sequences. Often medical geneticists must review DNA sequences by eye to try to identify or confirm mosaic mutations--a time-consuming endeavor fraught with the possibility of error. Writing in the January 2, 2023, issue of Nature Biotechnology, researchers from the University of California San Diego School of Medicine and Rady Children's Institute for Genomic Medicine describe a method for teaching a computer how to spot mosaic mutations using an artificial intelligence approach termed "deep learning."