Deep genomics of type 2 Diabetes
Type 2 diabetes mellitus has been at the forefront of human diseases and phenotypes studied by
new genetic analyses. Thanks to genome-wide association studies, we have made substantial
progress in elucidating the genetic basis of type 2 diabetes.
This tutorial summarizes the concept, history, and recent discoveries produced by genome-wide association studies for type 2 diabetes and glycemic traits, with a focus on the key notions we have gleaned from these efforts. Genome-wide association findings have illustrated novel pathways, pointed toward fundamental biology, confirmed prior epidemiological observations, and provided possible targets for pharmacotherapy and pharmacogenetic clinical trials.
Deep neural networks can be trained to detect Type 2 diabetes using a few biochemical and anthropogenic measurements. Such a network can then be used to predict onset of diabetes in other patients with access to these parameters. The tutorial will discuss a few other applications of deep learning in genomics of type 2 diabetes.
Outline/Structure of the Talk
A detailed Jupyter notebook based presentation on
- Identification of critical genes related to T2D based on GWAS studies
- Innovative genomic data visualation such as
- word cloud for implicated pathways
- genomic localization of T2D genes and pathways
- network analysis plans
- Deep learning for detection and prediction of T2D based on biochemical and anthropogenic measurements
- Conclusions and road ahead
Understanding of how machine learning and deep learning can be applied in detection and eventually prevention of type 2 diabetes.
AI developers, Healthcare professionals
Prerequisites for Attendees
- Exposure to Machine Learning
- Exposure to Deep Learning
- Exposure to genomics