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.

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A cure for cancer has been brought one step closer after scientists used artificial intelligence to develop a new technique that can predict how the disease can evolve – and therefore intervene earlier before a patient's cancer becomes drug resistant. The technique, known as Revolver (Repeated evolution of cancer), picks out patterns in DNA mutation within cancers and uses the information to forecast future genetic changes. It could help doctors design the most effective treatment for each patient and boost their chances of survival. The research team, led by the Institute of Cancer Research London (ICR) and the University of Edinburgh, also found a link between certain sequences of repeated tumour mutations and survival outcome. This suggests that repeating patterns of DNA mutations could be used as an indicator of prognosis, helping to shape future treatment.

Mutation Mitochondrial DNA (mtDNA) is a separate genome found in eukaryotic cells that is maternally inherited. Mutations in mtDNA underlie several human diseases, and the accumulation of these mutations has been associated with aging. Arbeithuber et al. used duplex sequencing to trace accumulation of spontaneous mtDNA mutations in oocytes, brain, and muscle cells of mice. Ten-month-old mothers showed a two- to threefold increased rate of mtDNA mutation compared with their 1-month-old pups. The authors found that the D-loop, a stretch of triple-stranded highly variable DNA in the noncoding region of the circular mtDNA where replication initiates, accumulated the most mutations. These mtDNA mutations occurred in patterns, indicating that they were caused by replication errors. It is possible that inheritance of aged mtDNA from older mothers may have health consequences for their offspring. PLOS BIOL. 18 , e3000745 (2020).