Prediction of complex diseases and related traits using deep learning and genomics
Approved Research ID: 87081
Approval date: September 8th 2022
Type 2 Diabetes (T2D) is a growing global epidemic according to the World Health Organization. Fortunately, T2D can be prevented and even reversed through medical intervention and changes in lifestyle and diet during early stages. Using genomic and environmental data from the UK biobank, we seek to understand the role that genes, the environment and their interactions play in the development of complex diseases, such as T2D. Our project aims to generate predictive models using modern algorithms of artificial intelligence to detect T2D even before the disease develops, thus increasing the odds of successful prevention or delay of the disease. In addition, these algorithms will be used to predict related traits such as obesity, Body Mass Index (BMI), and glucose levels, among others. Our results will shed light on the importance of genetic and environmental factors in disease predisposition allowing us to apply this methodology to other complex diseases of public health interest. The project will be developed approximately in three years.