Online Course: Machine Learning for Multi-Omics Integration

Dates: 2–4 February 2026

Overview:

With the explosion of Next-Generation Sequencing (NGS) technologies, researchers are generating vast and diverse biological datasets. Integrating multi-omics data is key to unlocking complex biological insights not accessible from single data types alone. This three-day online course provides a comprehensive introduction to machine learning methodologies tailored for multi-omics integration.

Participants will learn both theoretical concepts and practical skills through lectures and hands-on labs, covering state-of-the-art tools and methods to design and implement integrative analyses for their research.

Target Audience & Prerequisites:

Biologists, bioinformaticians, and biomedical researchers with basic familiarity with UNIX and beginner-level skills in R and/or Python programming will benefit most from this course.

Learning Outcomes

By the end of the course, participants will be able to:

• Understand fundamental machine learning techniques for biological data analysis

• Apply bioinformatic tools and best practices for multi-omics integration

• Design integrative analysis projects with appropriate methodologies

• Confidently adopt novel approaches to address complex biological questions

Program Schedule (Berlin Time)

Day 1: Introduction & Supervised Integration (14:00–19:00)

• Course overview and introductions

• Fundamentals of multi-omics integration

• Feature selection and supervised methods

• Hands-on labs: LASSO, PLS, LDA, mixOmics, DIABLO

Day 2: Unsupervised & Deep Learning Approaches (14:00–19:00)

• Unsupervised integration techniques

• Hands-on labs: MOFA1, MOFA2

• Deep learning for biological data integration

• Autoencoder applications

Day 3: Single-cell Omics Integration (14:00–19:00)

• Dimensionality reduction and visualization with UMAP

• Comparative analysis: PCA, tSNE, UMAP (lab)

• Batch correction and feature integration

• Hands-on labs: Seurat CCA, DTW, WNN

• Final discussion and Q&A

Registration

This course is fully online to encourage international participation.

For registration and more details, please visit: https://www.physalia-courses.org/courses-workshops/multiomics/