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
