https://deisygysi.github.io/categories/precision-medicine/Recent content in Precision Medicine on Wonderland of Deisy GysiHugo -- gohugo.ioenTue, 01 Sep 2026 00:00:00 +0000https://deisygysi.github.io/projects/network-medicine/Tue, 01 Sep 2026 00:00:00 +0000https://deisygysi.github.io/projects/network-medicine/Summary Diseases are rarely caused by isolated genes. They emerge from perturbations that spread through interconnected molecular, cellular and physiological systems. Network medicine provides a framework for representing those systems and testing how their structure relates to disease mechanisms, comorbidities, diagnosis and treatment. Our work expands the molecular maps used by network medicine and develops quantitative approaches for finding disease modules, comparing conditions and generating clinically relevant predictions. We are particularly interested in interactions mediated by non-coding RNAs and in integrating molecular evidence with clinical and population-level data.https://deisygysi.github.io/projects/ai-healthcare/Tue, 01 Sep 2026 00:00:00 +0000https://deisygysi.github.io/projects/ai-healthcare/Summary Artificial intelligence can help connect complex biomedical data to useful predictions, but high benchmark performance does not guarantee that a model will generalize to new molecules, populations or clinical settings. Our research examines both the opportunities and the limits of machine learning in healthcare. We combine AI with network-based representations to improve prediction, interpretability and evaluation. The emphasis is on methods that reveal the evidence behind a prediction, avoid shortcuts hidden in biomedical datasets and remain useful beyond well-annotated examples.