At the forefront of multi-omics, the <PMU> group has expertise in genomics, proteomics, and advanced data analysis to drive innovation in precision medicine.

We place maximum effort in automating processes, digitally linking all components of our work, ensuring that growth and updates are seamlessly integrated. This approach guarantees that no effort is wasted on routine tasks, allowing us to maintain our focus on subsequent expert analytical strategies.

Our analytical strategies are rooted in both classical and contemporary statistical methodologies, ensuring robustness and state-of-the-art precision. We often employ foundational models like linear regression, decision trees, and ensembles (random Forest, XGBoost) for their proven effectiveness and interpretability. Complementing these, we apply cutting-edge approaches including Bayesian inference models (multivariate, hierarchical, simulation-based, nonlinear, and nonparametric models), and Gaussian processes where necessary (neural networks, complex random walk processes). These advanced techniques allow us to model non-linear, high-dimensional data with a focus on uncovering latent structures and predictive accuracies in the multifaceted relationships of y = f(x). The choice of statistical model is carefully tested and tuned to represent the true underlying functional relationships, providing solutions that lead the field of precision medicine.

Technical details can be found in the DOCS pages. To contact us about pipeline code, please visit https://github.com/precisionmedicineunit project repositories. For other general contact, please vist the PrecisionMedicineUnit.ch homepage.