Dear all,

we are pleased to inform you that registrations are now open for the 2nd edition of the online course: Machine learning for multi-omics integration

Dates: 9–11 June

Course website: https://www.physalia-courses.org/courses-workshops/multiomics/

Time: 2:00–8:00 PM (Berlin time)

Course Overview:

Next-Generation Sequencing (NGS) technologies have enabled the generation of large and diverse biological datasets. Integrating multi-omics data using machine learning offers powerful opportunities for uncovering complex biological insights. This course will provide an introduction to current machine learning approaches for multi-omics integration, including supervised and unsupervised methods, as well as deep learning strategies.

Target Audience:

This course is intended for researchers, bioinformaticians, and data scientists with a basic understanding of the UNIX environment and beginner-level experience in R and/or Python programming.

Learning Outcomes:

• Understand core machine learning concepts applied to biological data

• Gain familiarity with tools and best practices for multi-omics integration

• Learn how to design and implement integrative analysis workflows

• Explore methods for both bulk and single-cell omics integration

• Acquire the skills to select appropriate approaches for specific research questions

Course Program:

Day 1

• Introduction to omics integration and machine learning approaches
• Supervised omics integration: feature selection, PLS, DIABLO

Day 2

• Unsupervised omics integration: MOFA

• Integration using deep learning and autoencoders

Day 3

• Omics integration in single-cell biology

• UMAP for dimensionality reduction and data integration