EE-626 / 2 credits

Teacher: Thanou Ntorina

Language: English

Remark: Next time: Fall 2024


Every year


Systems of interacting entities, modeled as graphs, are pervasive in biology and medicine. The class will cover advanced topics in signal processing and machine learning on graphs and networks, and will showcase applications of the tools in biomedicine.



Graph representation learning, machine learning, network science, biology, medicine.

Learning Prerequisites

Required courses

Good knowledge of machine learning; strong interest in biology and medicine; basics of graph theory, graph signal processing and graph machine learning are desirable but not necessary; Python.

Learning Outcomes

By the end of the course, the student must be able to:

  • Explore recent developments in graph machine learning for biology and medicine
  • Brainstorm on future developments of these tools in other medical or biological applications
  • Apply graph machine learning and signal processing tools to their own biomedical research
  • Analyze and summarize scientific work
  • Synthesize arguments into scientific presentations

Assessment methods

Oral exam.



- Li, M., Huang, K., and Zitnik, M., Graph representation learning in biomedicine and healthcare, Nature Biomedical Engineering, 6, 1353-1369, 2022.

- Barabási, A.-L. Network medicine — from obesity to the “diseasome”. N. Engl. J. Med. 357, 404–407 (2007)

Ressources en bibliothèque

Moodle Link

In the programs

  • Number of places: 48
  • Exam form: Oral (session free)
  • Subject examined: Graph representations for biology and medicine
  • Lecture: 28 Hour(s)

Reference week

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