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Omics Integration and Systems Biology

Course open for PhD students, postdocs, and researchers looking for an introduction to multi-omics integration and systems biology.

Course homepage

Important dates

Application now open!

Apply here

Application closes: 21 August, 2020

Confirmation to accepted students: 28 August, 2020

For questions about the course, please contact Rui Benfeitas (, Nikolay Oskolkov (, or Ashfaq Ali (

Course content

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.

Some of the covered topics include:

  • Data wrangling in omics studies;
  • 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

Further details about the course content may be found on the course homepage

Entry requirements

The course is aimed at M.Sc., PhD- or postdoc-level researchers with basic programming experience (e.g. R, Python, Matlab). We will not discuss how to process the raw omics data and the students are referred to other NBIS courses for this matter.


Basic programming experience in either R, Python, UNIX/Bash, or Matlab

Basic understanding of frequentist statistics

Ability to bring your own laptop with R, Python and Matlab installed for the practical exercises


Experience with analysis of omic data (e.g. metabolomics, proteomics, transcriptomics) and NGS analysis

Differential expression/abundance analysis

Completing NBIS courses “Introduction to Bioinformatics using NGS data”, “Introduction to biostatistics and machine learning”

Course fee 

A course fee of 2000 SEK will be invoiced to accepted participants. This includes lunches, coffee, snacks and course dinner. Please note that NBIS cannot invoice individuals.

Link to application