SHUA13043U Course in Bioinformatics and Systems Biology

Volume 2024/2025
Education

MSc in Human Biology - compulsory

Content

Bioinformatics plays a decisive role in molecular biology, but has also become a key topic in medical research. The focus in bioinformatics is on integrative analysis of high-throughput biological data, ranging from molecular-level data on sequences and macromolecular structures to clinical data. A major aim of this is to gain insights into diseases, from rare to complex, such as diabetes and cancer. The objectives of the course are to provide the students knowledge of a range of methods for finding, analyzing and integrating heterogeneous biological data in the context of a specific disease, and to provide them with the necessary foundation to critically evaluate results of such analyses.

Learning Outcome

After completing the course the student is expected to:

Knowledge

  • Discuss how the information from biological experiments may be represented in an electronic format
  • Describe the basic principles in genetic sequence analysis like Genome Wide Associations Studies (GWAS) and Polygenetic Risk Scores (PRS) and explain how these contribute to precision medicine
  • Describe how to model complex biological systems as networks
  • Discuss use cases of Machine Learning, Deep Learning and Neural Networks in clinical aspects
  • Describe how to mine knowledge from biomedical data

 

Skill

  • Search for data in publicly available databases such as STRING and DISEASES
  • Use data analysis programs such as Cytoscape and Disease Trajectory Browser
  • Produce and critically evaluate biological analyses like variant calling, functional enrichment and protein networks
  • Present heterogeneous data on a biological system for answering biological questions

 

Competence

  • Master a range of methods for finding, analyzing and integrating heterogeneous biological data in the context of a specific disease
  • Critically evaluate results of such analyses
None
Lectures and practical exercises.
  • Category
  • Hours
  • Lectures
  • 20
  • Preparation
  • 15
  • Exercises
  • 20
  • Total
  • 55
Written
Oral
Individual
Collective
Continuous feedback during the course of the semester
Credit
2,5 ECTS
Type of assessment
Requirement to attend classes
Type of assessment details
Participation in exercises and subsequent preparation of exercise reports
Aid
All aids allowed
Marking scale
passed/not passed
Censorship form
No external censorship
One internal examiner
Criteria for exam assesment

To achieve a course certificate, the student must be able to:

Knowledge

  • Discuss how the information from biological experiments may be represented in an electronic format
  • Describe the basic principles in genetic sequence analysis like Genome Wide Associations Studies (GWAS) and Polygenetic Risk Scores (PRS) and explain how these contribute to precision medicine
  • Describe how to model complex biological systems as networks
  • Discuss use cases of Machine Learning, Deep Learning and Neural Networks in clinical aspects
  • Describe how to mine knowledge from biomedical data

 

Skill

  • Search for data in publicly available databases such as STRING and DISEASES
  • Use data analysis programs such as Cytoscape and Disease Trajectory Browser
  • Produce and critically evaluate biological analyses like variant calling, functional enrichment and protein networks
  • Present heterogeneous data on a biological system for answering biological questions
  • Master a range of methods for finding, analyzing and integrating heterogeneous biological data in the context of a specific disease
  • Critically evaluate results of such analyses