SMARTbiomed summer school 2026

About the course

The SMARTbiomed summer school in “Statistical Genetics, Causal Inference, and Multi-omics and Machine Learning” is designed to introduce researchers to modern methods of computational and statistical analysis of complex data. The module content will focus on statistical genetics, causal inference, machine learning based approaches including applications to multi-omics and clinical prediction. Modules will motivate methods developments through theory in lectures, but with clear applications in mind, laid out through extensive, hands-on practicals throughout the week. The summer school is also a fantastic opportunity to broaden your professional network and to engage with the experts that are part of the SMARTbiomed Pioneer Centre, principly split between researchers in the UK and Denmark.

Find out more about the Pioneer Centre for SMARTbiomed here: https://smartbiomed.dk/.

Participants

Any junior researcher from across the globe interested in the theory, development, and real world application of modern methods of computational and statistical analysis to complex datasets.

Courses

The summer school will consist of three courses, outlined below. Click on each course title to read more details about its content:

Statistical genetics

This course will serve as an introduction to the foundational concepts and methods in statistical genetics, providing participants the motivation and theory behind key approaches, as well as practical experience in handling and analysing genetic data.

Topics will include an exploration of the principles of common and rare variant association testing, polygenic risk score estimation, heritability and genetic correlation estimation.

Causal inference

This course will introduce foundational concepts in causality and causal inference, providing participants the background reasoning needed to form estimands and discuss estimators. We will then build on this foundational knowledge, focusing on methodology through example-driven applications to real-world datasets, rather than language or concepts.

Machine Learning with applications to multi-omics and clinical prediction

The first part of the course will provide a general introduction to clinical prediction models. Participants will get an overview of the health AI landscape including different data modalities, and clinically relevant concepts like algorithmic fairness. The second part of the course will focus on multi-omics data modalities; including proteomics, lipidomics and transcriptomics. We will introduce foundational machine learning concepts, including supervised and unsupervised learning, dimensionality reduction, clustering, key learning algorithms. We will explore how machine learning can be applied to such multi-omics and multi-modal data, with an emphasis on advancing our understanding of human diseases and enabling more precise diagnostics, prevention, and treatment strategies.

Guest lectures

Each day will culminate with a guest lecture, delivered by an expert from one of the three courses, to encourage cross-talk between attendees. These lectures will focus on cutting edge applications of methods and principles laid out across the week, to drive discussion.

Timetable