Our group at the Singapore Institute for Clinical Sciences and the University of Manchester have received funding from the Wellcome Trust to investigate the machine learning modelling techniques for high-dimensional measurements from population studies. Recently, high-throughput 'omics technologies have allowed us to collect large numbers of molecular measurements from a single sample. Analysing these large datasets poses a problem, because existing techniques do not allow us to analyse all measurements jointly as outcomes. We will develop a statistical technique for jointly analysing large numbers of molecular measurements, using recent advances in statistical inference, as well as applying prior knowledge to reduce the number of relationships between measurements that needs to be explored. Additionally, we will develop a method for designing longitudinal studies to gain optimal information about these high-dimensional outcomes. The benefits of our approach will be two-fold: First, in allowing us to gain additional information about the relationships between longitudinal measurements, and second in improving the design of future studies, which will lead to time and cost savings.