This is an online event. Tune in via Webex here.
Predicting drug combination response surfaces
Drug combinations are required in the treament of many complex diseases. Combination treatments can enhance efficacy and reduce toxicity by lowering the doses of single drugs, and they often benefit from the synergistic action of the drugs. As drug combination screening experiments are costly and time-consuming, reliable machine learning models are needed for prioritizing potential combinations for further studies.
In my presentation I will walk through the prediction tasks and recent machine learning technologies for predicting drug combination responses in cancer, including our recent work on kernel-based response surface prediction (Huusari et al. 2025).
About the speaker
Juho Rousu is a Professor of Computer Science at Aalto University, specialising in machine learning and computational biology. His research focuses on developing principled methods built on regularised learning, kernels, sparsity, and optimisation techniques. He has a particular interest in learning with multiple and structured targets, multiple views and ensemble methods. He applies these approaches to challenges in metabolomics, biomedicine,pharmacology, and synthetic biology.
The Causal Analysis of Biomedical Data Lecture Series is supported by the Luxembourg National Research Fund (FNR) RESCOM Program.
