Statistical methods for the analysis of complex omics data

Abstract

The major challenge in analysing omic datasets is the strong dependencies which are present between samples and features. Taking into account and modelling the different dependency structures can lead to further improvements of our knowledge of the biological mechanisms. Therefore, improving our ability to predict diseases. This dissertation focuses on the development of new statistical methods designed to take into account the existing structures inside omic datasets by using mixed models, Gaussian graphical models, and machine learning approaches.