Major biotechnological breakthroughs in the past two decades have made it possible to collect extremely large amounts of data at a multitude of different levels. In biomedical research, for example, so-called "omics data" are collected, and provide a comprehensive picture of various molecular states and processes - for example, at the levels of the genome, epigenome, transcriptome, proteome, metabolites (metabolome) and their dynamics, as well as using high-resolution imaging. Data integration across all these levels, so-called multi-omics analyses, open new perspectives on our understanding of pathophysiological mechanisms and thus on the development of diseases, but at the same time entail significant bioinformatics challenges. Moreover, at all these levels, new opportunities are currently emerging for the collection of data at the single cell level. Modern medicine is thus increasingly driven by big data. Efficient use of these data for biomedical research can only be implemented with data science methods. Such multimodal data analyses allow a more comprehensive understanding of the molecular processes involved in the transition from health to disease. With the integration of artificial intelligence, the insights gained can be leveraged to develop new ways to predict and prevent disease, enabling rapid translation into clinical practice.
The Multi-Omics Data Science (MODS) initiative addresses all these developments by building a bridge from the university's AI strategy on the one hand to the established research focus areas of the University Hospital Düsseldorf on the other. Our focus is on metabolic disorders such as obesity, dyslipidemia, fatty liver disease, diabetes mellitus and their cardiovascular and neurological disease manifestations, which are leading drivers of mortality in our society, e.g. from heart attack, stroke and heart failure. Thus, new data-driven concepts will be harnessed to maintain population health and tailor therapies for at-risk groups and those with manifest disease. The overall goal is an integrative investigation of the interaction of cardiovascular, neuronal and metabolic systems (interorgan crosstalk) with a transdisciplinary approach.
The concept of the MODS initiative aims at fostering collaboration through tandem projects between computational and biomedical PIs. Beyond the tandem projects and the common machine learning platform, MODS will organize a common biweekly lecture series, an annual workshop, as well as a hackathon twice a year in which all scientists funded by the project work together on a data science topic for one week. Collaboration on clinical translation is achieved through a Data Science Integration Board.