Omics Integration and Systems Biology
Next edition: 19 - 23 April 2021 (more information to come soon). Want to be notified of the course application opening? Sign up here
5 - 9 October 2020
Lund University, Department of Biology
The aim of this workshop is to provide an integrated view of biological network construction and integration, constraint-based modelling, multi-omics integration through Machine Learning, and data-driven hypothesis generation. A general description of different methods for analysing different omics data (e.g. transcriptomics and genomics) will be presented with some of the lectures discussing key methods in their integration. The techniques will be discussed in terms of their rationale and applicability, with a particular focus on possible confounding factors. The course will also include hands-on sessions and invited speaker seminars.
Audience Course open for PhD students, postdocs, and researchers looking for an introduction to multi-omics integration and systems biology.
Fee A course fee of 1500SEK will be invoiced to accepted participants. This includes lunches, and coffee breaks. Please note that NBIS cannot invoice individuals.
- Condition-specific and personalized modeling through Genome-scale Metabolic models based on integration of transcriptomic, proteomic and metabolomic data;
- Biological network inference, community and topology analysis and visualization
- Identification of key biological functions and pathways;
- Identification of potential biomarkers and targetable genes through modeling and biological network analysis;
- Application of key machine learning methods for multi-omics analysis including deep learning;
- Multi-omics integration, clustering and dimensionality reduction;
- Similarity network fusion and Recommender systems;
- Integrated data visualization techniques
At the end of the course, students should:
- Be able to integrate different omics and simulate biological functions using constraint-based models and FBA.
- Build biological networks based on different omics data, as well as integrated multi-omics networks;
- Use different biological network analysis techniques to compare different cell-types or conditions (e.g. disease vs healthy);
- Identify key methods for analysis and integration of omics data based on a given dataset;
- Understand strengths and pitfalls of key machine learning techniques in multi-omic analysis;
- Be aware of important confounding factors and sources of bias.