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AI can be used in Genetics for identifying and treating genetic diseases by providing accurate predictions and reducing the risk of ineffective treatment.
FREMONT, CA: Genome editing is both a delicate and a powerful finding. It's been a source of debate, discussion, and, despite flaws, excitement, and invention for a long time. By taking what is already known and utilizing that data to develop realistic and well-informed predictions, machine learning has the potential to enhance patient outcomes and reduce the risk of inefficient treatment and incorrect diagnosis for patients with genetic illnesses. In genetics, AI has two key applications: identifying dangerous genes and treating disease.
Analyzing the large quantity of data stored in a single person's DNA is an exceedingly tedious and time-consuming operation for humans. This analysis may be made considerably more efficient and accurate by using machines for what they were designed for: to make difficult work easier. Machine learning algorithms can be used to compare gene expression levels in malignant and normal tissue samples of a cancer patient's DNA to predict which genes have been altered in that patient's DNA. The algorithms would learn and generate these predictions by comparing how often a gene is expressed in a cancer sample to how often the same gene is expressed in a normal sample, adding new information with every new set of data provided to it.
The capacity to remove disease-causing genes is a key proponent of gene editing. While technologies like CRISPR have gone a long way, the danger of error remains high, and in order for gene editing to progress, safety must remain a key priority. Machine learning algorithms help determine where the change needs to be done and how to ensure that the DNA strand is properly repaired thereafter, lowering the risk of errors. AI is particularly beneficial in customized medicine, as medicines must be tailored to the specific demands of one patient vs another. AI can help determine which genes have been altered by deleterious mutations so that they may be targeted in gene therapy, similar to how genomics can help diagnose a disease.
Another area where AI could be useful is in DNA repair after modification. When the Cas9 enzyme alters a DNA strand, the strand makes automatic repairs, and evidence suggests that these repairs are reliant on the guide RNA utilized. Algorithms designed to estimate the repairs that would be done based on the altered sequence can increase the precision with which guide RNAs cause specific mutations.