We are pleased to announce the upcoming in-person course: Machine Learning for Multi-Omics Integration, hosted at the University of Barcelona, Faculty of Biology (IRBio).
Dates: 15–17 December 2025
Location: Dept. Evolutionary Biology, Ecology & Environmental Sciences, University of Barcelona, Spain
Course website: https://www.physalia-courses.org/courses-workshops/multiomics-barcelona/
Course overview:
This course will provide participants with an introduction to machine learning methodologies for the integration of multi-omics datasets. Through a combination of lectures and hands-on practicals, attendees will learn supervised, unsupervised, and deep learning approaches for multi-omics integration, as well as methods for single-cell omics data.
Target audience:
The course is aimed at researchers with basic familiarity in R and/or Python, and some awareness of UNIX. It is suitable for graduate students, postdocs, and professionals working with biological or biomedical big data.
Learning outcomes:
By the end of the course, participants will:
Understand the basics of ML approaches for biological data analysis
Gain an overview of tools and best practices for multi-omics integration
Learn to design and implement integrative projects using appropriate methodologies
Explore supervised, unsupervised, and deep learning integration tools, including mixOmics, DIABLO, MOFA, and autoencoders
Apply methods for single-cell omics integration
