Gene editing technology is getting better and growing faster than ever before. New and improved base editors—an especially efficient and precise kind of genetic corrector—inch the tech closer to treating genetic diseases in humans. But, the base editor boom comes with a new challenge: Like a massive key ring with no guide, scientists can sink huge amounts of time into searching for the best tool to solve genetic malfunctions like those that cause sickle cell anemia or progeria (a rapid aging disease). For patients, time is too important to waste.
Click here for original story, New machine learning model predicts which base editor performs best to repair thousands of disease-causing mutations
Source: Phys.org