Introduction of machine learning into the world of genomics has a huge role to play in preventing predisposition to illnesses such as cystic fibrosis, Huntington’s disease, sickle cell anemia, etc. In combination with gene editing technologies like CRISPR, Neuron Storage™ is primed to help scientists prevent susceptibility to life-threatening genetic diseases like these and help clinicians devise more personalised management plans. 

Consultant firm Frost & Sullivan estimates AI is poised to help the healthcare industry see big financial gains and estimates that in 2021 AI generated revenues will reach $6.7 billion globally. A major reason for these gains is how rapidly AI allows mapping and editing of the human genome which has implications in targeting drug design, prevention and possibly curative effects of lifelong disease. 

The purpose of clinical genetics is to help diagnose and forecast future disease risk. This is traditionally a manpower intensive process often complicated by the fact that many common diseases are a result of a complex interplay between inherited genetic risk factors, environmental exposures and behaviors. Genetic risk alone provides a baseline estimate of lifetime risk for disease but does not paint the entire picture. Instead, researchers must take into account modifiable risk factors to achieve maximum predictive accuracy. For example, breast cancer risk is associated with several non-genetic risk factors including breast tissue density (determined by mammography), age at first birth, age at menarche, and age at menopause. 

Combining data surrounding these genetic and non-genetic risk factors has been proven to significantly improve the predictive accuracy of breast cancer risk models leading to better informed risk-based mammographic screening strategies. AI algorithms, excel at unraveling the complex interplay between genetic data, historical EMR data, real-time digital health monitoring devices, etc. Utilizing the technology behind Neuron Storage™, AI integration of a variety of healthy data types and risk factors into comprehensive predictors of disease is a compelling prospect for the future of genotype-to-phenotype predictions.