{"id":9596,"date":"2023-01-05T06:00:00","date_gmt":"2023-01-05T00:30:00","guid":{"rendered":"https:\/\/cbirt.net\/staging\/?p=9596"},"modified":"2023-04-06T16:09:14","modified_gmt":"2023-04-06T10:39:14","slug":"meet-deepmosaic-a-computer-program-that-learns-to-identify-mosaic-mutations-causing-disease","status":"publish","type":"post","link":"https:\/\/cbirt.net\/staging\/meet-deepmosaic-a-computer-program-that-learns-to-identify-mosaic-mutations-causing-disease\/","title":{"rendered":"Meet DeepMosaic: A Computer Program that ‘Learns’ to Identify Mosaic Mutations Causing Disease"},"content":{"rendered":"\n

Scientists from the University of California San Diego School of Medicine and Rady Children’s Institute for Genomic Medicine have developed a method “DeepMosaic<\/em>” 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.\u00a0<\/em><\/p>\n\n\n\n

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. These mutations<\/a> can cause a variety of disorders but have been challenging to detect due to their rarity.<\/p>\n\n\n\n

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What is a mosaic mutation?<\/h2>\n\n\n\n

A mosaic mutation is a genetic change that occurs during embryonic development. Mosaic mutations are not present in every cell of the body and are often only present in a subset of cells. These mutations can arise due to errors in DNA replication or environmental factors such as exposure to radiation or certain chemicals. Mosaic mutations are thought to play a role in the development of a variety of diseases and disorders, including cancer<\/a> and certain neurological conditions.<\/p>\n\n\n\n

Detecting DNA mutations<\/h2>\n\n\n\n

Current software’s for detecting DNA mutations are ineffective at identifying mosaic mutations within normal DNA sequences. As a result, medical geneticists often have to manually review DNA sequences, which is time-consuming and prone to error. Mosaic mutations, which occur after fertilization, have been linked to various diseases.<\/p>\n\n\n\n

Deep learning, a type of machine learning<\/a> involving artificial neural networks, allows computers to learn from large amounts of data in a similar way to how humans learn. This approach can be used to process visually represented data and has resulted in significant advances in computational abilities, including the detection of mutations. Deep learning models can process data with high accuracy and attention to detail, similar to how the human visual system works.<\/p>\n\n\n\n

According to Joseph Gleeson, senior study author, and director of neuroscience research at the Rady Children’s Institute for Genomic Medicine, focal epilepsy is an example of a disorder that has yet to be fully understood.<\/p>\n\n\n\n

Epilepsy affects a significant portion of the population, and a quarter of patients with focal seizures do not respond to traditional medications. In these cases, surgical removal of the affected part of the brain may be necessary to stop seizures. Mosaic mutations in the brain have been linked to the development of epileptic focus.<\/p>\n\n\n\n

Previously, in many patients, the cause of epilepsy could not be identified, but the application of DeepMosaic allowed the mutation to be easily identified. Accurate detection of mosaic mutations is essential for medical research in the development of treatments<\/a> for various diseases.<\/p>\n\n\n\n

DeepMosaic’s Approach<\/h2>\n\n\n\n

The computer program DeepMosaic was trained on 180,000 simulated and experimentally assessed mosaic mutations and was tested on additional simulated and biologically tested mutations from a variety of genomic and exomic sources.<\/p>\n\n\n\n

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