Multi-Omics Data Integration: Computational Approaches and Network-Based Analysis

Trainers:

• Carme Zambrana, Barcelona Supercomputing Center (BSC-CNS), Spain
• Anđelka Zečević, Serbian Academy of Sciences and Arts (SASA), Serbia
• Milica Galjak, Faculty of Mathematics, University of Belgrade, Serbia
• Igor Davidović, Institute of Molecular Genetics and Genetic Engineering, University of Belgrade, Serbia

This two-day workshop introduces computational approaches for the integration and interpretation of multi-omics datasets.

The workshop is intended for participants with prior experience in R and/or Python programming and basic bioinformatics analysis

On Day 1, participants will attend introductory sessions covering the mathematical and programming foundations required for computational multi-omics analysis, including basic linear algebra concepts and introductory Python programming.

On Day 2, Carme Zambrana will lead a hands-on workshop focused on computational strategies for integrating heterogeneous omics datasets, including genomics, transcriptomics, proteomics, and metabolomics data. The first part focuses on matrix factorization approaches, including Non-negative Matrix Tri-Factorization (NMTF) and Multi-Omics Factor Analysis (MOFA), which enable the integration of multiple high-dimensional datasets and identification of shared and dataset-specific biological patterns. The second part introduces network-based approaches for modelling biological systems as interconnected entities. Participants will explore graph-based methods, including community detection and multilayer community trajectory analysis, for uncovering relationships across omics datasets.

Target audience:
Master and PhD students, as well as researchers in bioinformatics, computational biology, and medical informatics interested in multi-omics data integration.

Materials:
Workshop materials and practical examples will be provided to participants.

Register here