Graph representations for biology and medicine

Summary

Students will learn how to capture complex interactions in biology and medicine through networks, and how such a general data representation framework can lead to novel discoveries. 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. This course will be held as an advanced seminar, which will familiarize students with recent developments in the topic, through a combination of lectures on some fundamentals on processing and analyzing data on graphs, and the presentation of original research articles that make use of these tools for scientific advances in biology and medicine.  Students will learn to interact with scientific work, analyze and understand strengths and weaknesses of scientific arguments, summarize essential ideas, and develop them further in their research. Besides obtaining a good understanding of graph AI tools for biomedicine, the students will improve their presentation and communications skills.

- Introduction into network medicine, graph processing and representation learning for biology and medicine
- General introduction to recent developments in graph theory, graph machine learning, graph signal processing, graph generative models
- Presentation of research articles in the following topics:
- Graphs for disease understanding (e.g., spatial biology, neuroscience)
- Graph generative models for medicine (e.g., therapeutic developments, synthetic data generation through diffusion models)
- Graphs for computational efficiency (e.g., 3D modeling of arteries, computational fluid dynamics)
- Graphs for multimodal data integration (e.g., knowledge graphs for knowledge discovery, design of novel clinical trials)

The basics of graphs and graph machine learning will be covered by the instructor in the first weeks of the course. After that, students will be expected to read, review, present, and discuss relevant research papers in one of the above-mentioned topics. Every week, one or more students will be responsible for reading one or more research papers, and prepare a presentation highlighting and discussing the important points. All students will be responsible for reading the paper, and contributing to the discussion of the papers' merits and weaknesses. The precise papers will be specified in the first two weeks of the semester, after taking into account the background and interests of the participants.