Next-Generation Sequencing (NGS) technologies have given rise to vast amounts of biological and biomedical Big Data. The rapidly growing volume and diversity of data present unique opportunities as well as significant challenges for analysis. Biological Big Data from different sources (Multi-Omics data) are a promising resource due to their synergistic effects, which can potentially model the behavior of biological cells. Omics integration can thus identify novel biological pathways that may not be distinguishable from individual Omics datasets alone. In this course, through a mix of lectures and hands-on sessions, we will cover machine learning methodologies for integrating large amounts of biological data.

We assume some basic awareness of UNIX environment, as well as at least beginner level of R and / or Python programming.

By completing this course, you will:

• Understand the basics of machine learning approaches to biological data analysis
• Gain an overview of bioinformatic tools and best practices for integrative Omics analysis
• Be able to design an integrative project and implement appropriate analysis methodologies
• Be able to choose the right tools and approaches to answer your specific research question
• Gain confidence in learning new methods needed to answer your research question

Day 1: 2-8 PM Berlin time
A) Introduction to Omics integration and machine learning approaches to Omics integration
B) Supervised Omics integration: feature selection, PLS / DIABLO

Day 2: 2-8 PM Berlin time
A) Unsupervised Omics integration: MOFA
B) Integration via deep learning / Autoencoder

Day 3: 2-8 PM Berlin time
A) Omics integration for single-cell biology
B) UMAP for Omics integration

Should you have any further questions, please send an email to info@physalia-courses.org