Prof. Dr. Bernhard Renard

Using machine learning and multi-layer molecular networks to predict drug response

Due to the complexity of biological systems, finding the right treatment for a specific disease can be challenging. Computational models can help our understanding of the effects of drugs and make them amenable to analysis. Thus, we build computational representations of these complex biological systems by modeling them as large networks that capture the interactions between the molecules in cells. We use network science and machine learning approaches to analyze these networks. For example, we employ graph neural networks to learn meaningful patterns that can help in predicting treatment outcomes. We aim to consider and combine the multiple layers of information that are obtained for different types of molecules, such as proteins and mRNAs. Since many diseases are treated with multiple drugs at the same time, we are also working on building a framework to computationally analyze the response to multiple drugs.

Check out the manuscript [link] and R package at CRAN [link] to DrDimont, one of our tools for explainable multi-omics network-based prediction.