Omics Integration and Systems Biology course at Lund University!

The aim of this workshop is to provide an integrated view of data-driven analysis of biological data through machine learning, graph and network analysis as well as constraint-based modeling integration methods. A general description of different approaches for working with multiple layers of biological information, i.e. Omics data (e.g. transcriptomics and genomics) will be presented with some of the lectures discussing their advantages and pitfalls. The techniques will be discussed in terms of their rationale and applicability.

Topics covered include:

• Data pre-processing, cleaning and feature selection prior to integration;
• Application of key machine learning methods for multi-omics analysis including deep learning;
• Multi-omics factor analysis, dimension reduction and clustering;
• Single Cell and Spatial transcriptomics integration;
• Biological network inference, community and topology analysis and visualization;
• Condition-specific and personalized modeling through Genome-scale Metabolic models for integration of transcriptomic, proteomic, metabolomic and fluxomic data;
• Identification of key biological functions and pathways;
• Identification of potential biomarkers and targetable genes through modeling and biological network analysis;
• Application of network approaches in meta-analyses;
• Similarity network fusion and matrix factorization techniques;
• Integrated data visualization techniques