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.

Deep learning and machine learning have infested every known field in last couple of years. Healthcare has not remained immune to it either. However, there is much more to healthcare than improve AUC and reducing errors. The cost can be too high. This tutorial discusses trends of using AI in healthcare, from automating electronic health records and using it to predicting patientcare, radiology, ophthalmology to genomics, other omics and eventually personalized medicine. Once the AI powered healthcare starts yielding results "better than" doctors, then the clinical deployment becomes the next critical stage. Clinical judgement involves clinical research, experience and other supporting sciences. The presentation discusses from a generic ML/DL workflow, how unintentional small errors in each step can lead to spurious predictions.

This tutorial will trace the journey of possibilities for deep learning in healthcare and how an integrated, holistic use will assist doctors and hospitals in providing targeted healthcare.