séminaire DKM : Sequencing in Health, a decade of disappointment. Possible AI solutions.

The recent increase in the amount of sequencing data has produced a remarkably low number of clinically actionable discoveries. The main issues in translating these data into outcomes come from the fact that many diseases are multifactorial requiring complex models and large sample sizes to understand them. However, sequencing data are large, sensitive and heterogeneous. In this talk, I will propose some solutions to address multiple issues pertaining to the use and interpretation of health-related sequencing data. Specifically, how can we explore sequencing without a reference genome? How can we realistically preserve patient identity? My talk will also introduce some basic deep learning architectures for an audience without a background in machine learning.