Integrative data analysis by combining networks, dynamical models and machine learning

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Combining different views from complementary data layers is key for robust predictions in biomedical research. However, it is still challenging to incorporate dependencies and relationships into coherent modeling and prediction frameworks, especially when applying machine learning (ML). I focus on two approaches that I combine with ML: Network-based methods allow for representing and harnessing the interaction of entities and data layers, and dynamical models can represent temporal properties and intricate dependencies of the investigated processes. In this talk, I will present recent examples of our developed methods for integrative data analysis. These range from differential integrated multi-omics networks, over neural networks with (multi-)graph input, to infusing prior knowledge from dynamical models into ML. We tackle problems such as drug response prediction or epidemic time series forecasting in scenarios with sparse data.

Katharina Baum is Assistant Professor at the Free University Berlin, and Adjunct Assistant Professor at the Hasso Plattner Institute at Mount Sinai and the Windreich Department for Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai. After studying mathematics at Humboldt University Berlin and Ecole Polytechnique, France, she pursued a PhD in dynamical modeling of biological processes with a fellowship from the Max Delbrück Center for Molecular Medicine in the Helmholtz Association. She conducted a postdoc project at Luxembourg Institute of Health in close cooperation with Nanyang Technological University, Singapore. Therein, she developed network-based modeling approaches to analyze biomolecular multi-omics data. In 2020, Katharina Baum joined the Hasso Plattner Institute for Digital Engineering, Potsdam where she headed the group Network-based Data Analysis as Senior Researcher before moving to Free University Berlin in 2023. Katharina Baum focuses on integrative data analysis method development. She combines networks, dynamical modeling, and machine learning. Her main interest lies in integrating multi-layered data to scrutinize intra- and intercellular molecular processes and to predict drug response or drug-drug interactions.