The aim of this project is to provide a complete framework for the analysis of high-dimensional time-series data with multiple diverse data types with the goal of gaining insights and making predictions about disease phenotypes, disease progression and response to treatment. The proposed developments range from methods focusing on the integration of multiple, possibly very different data types, to advanced machine learning methods that allow time-varying analyses of complex biomedical multimodal data.
SNF Grant Proposal Accepted!
Our SNF Grant Proposal: “Machine Learning Methods for Clinical Data Analysis and Precision Medicine" got accepted!
