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.
While scanning the 3 billion bases of the human genome, current DNA mutation software detectors are not well suited to discern mosaic mutations hiding among normal DNA sequences.
As a result, 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.
Deep learning, sometimes referred to as artificial neural networks, is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example, especially from large amounts of information.
Compared with traditional statistical models, deep learning models use artificial neural networks to process visually represented data.
As a result, the models function similarly to human visual processing, with much greater accuracy and attention to detail, leading to significant advances in computational abilities, including mutation detection.