Project R-11362

Cooperation agreement between Hasselt University and VITO concerning the PhD of Dries Heylen related to "X omics data analysis and integration in precision health". (Research)

Throughout history, clinicians and medical professionals have used previously associated symptoms to diagnose different diseases and treat the patients correctly. In the 20th and 21st century when clinical biochemical tests became commercially available, they have been introduced as quantitative diagnostic tools to assist the medical professional to differentiate between health and disease. After incremental advancements in that domain, today there are 1000 tests developed and over 150 of them are in the routine category where many clinical laboratories perform on a daily basis to assist clinical decisions. Although this approach allowed us to achieve overall better health in the western world, there are a few hurdles in the system. The first one being, it is a slow and expensive method and still highly depends on doctors' expertise. Furthermore, the doctor needs to know the right set of tests to prescribe based on high level symptoms to make the correct diagnosis while the potential diseases a patient might have, are defined as independent entities sharing symptoms. Besides, the treatment options for a specific disease is usually the same for all patients, whereas the patients' profiles could be biologically very different. Recent progress in life sciences and technology allows us to map genetic, proteomics and metabolic profile of individuals from a limited amount of sample, revealing millions of molecular underpinnings of this sample and the individual it originates. This technology enriched with above mentioned clinical biochemistry measurements constitute an information rich medium to study health and disease. The aim of this project is to develop new methods as well as adapting established ones to integrate and visualize various levels of biological information at different scales (genome, proteome, metabolome and biochemical levels) as well as over time to describe individual health at the molecular level. We propose to use Graph Theory and describe the transition from health to disease as a molecular & phenotypical network evolving from one state to another. These methods will be particularly developed for and applied to the genomics, proteomics, metabolomics and clinical biochemistry data collected in a longitudinal cohort study of VITO: the I am Frontier cohort.

16 December 2020 - 15 December 2024