Section: Overall Objectives

Overall Theoretical Objectives

The overall objective of SISTM is to develop statistical methods for the integrative analysis of health data, especially those related to clinical immunology and vaccinology to answer specific questions risen in the application field. To reach this objective we are developing statistical methods belonging to two main research areas:

• Statistical and mechanistic modeling, especially based on ordinary differential equation systems, fitted to population and sparse data

• Statistical learning methods in the context of high-dimensional data

These two approaches are used for addressing different types of questions. Statistical learning methods are developed and applied to deal with the high-dimensional characteristics of the data. The outcome of this research leads to hypotheses linked to a restricted number of markers. Mechanistic models are then developed and used for modeling the dynamics of a few markers. For example, regularized methods can be used to select relevant genes among 20000 measured with microarray/RNA-seq technologies, whereas differential equations can be used to capture the dynamics and relationship between several genes followed over time by a q-PCR assay or RNA-seq. We apply the methods developed by the team in data science approaches to vaccine trials in order to elucidate the effects and mechanisms of action of vaccines and immunotherapies and to accelerate their clinical development.