Bioinformatics
This theme explores data science and AI methodologies in the context of advancing life sciences and healthcare. Technological advancements in molecular readout, synthesis, and editing have transformed the study of living organisms, necessitating new computational methods to unlock insights from high-dimensional molecular data and discover new possibilities in complex biological systems.
Year 1 |
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Quarter 1 |
Quarter 2 |
Quarter 3 |
Quarter 4 |
| Data management and Engineering | Software Engineering and Testing for AI Systems | Responsible Data Science and AI Engineering | Research course |
| Machine and Deep Learning | Theme 1 | Theme 1 | Theme 1 |
| Probabilistic AI and Reasoning | Theme 2 | Theme 2 | Theme 2 |
Credits: each course in a theme is 5EC, so each theme is 15EC.
Students choose 2 themes, each of which has 3 courses in the 2nd, 3rd and 4th quarters of the 1st year. For this theme, you will take the following courses:
Q2 - Algorithms for sequence-based Bioinformatics
This course provides a good understanding of algorithms and data structures in genomics used for DNA sequence analysis. You will learn to implement algorithms in python and to translate methods described in scientific literature into a working implementation. Bioinformatics application domains that are covered include: variant calling, motif detection, genome assembly, sequence alignment, and DNA sequencing.
Q3 - Algorithms for network-based Bioinformatics
This course covers a brief overview of molecular biology, the advent of high-throughput measurement techniques and large databases containing biological knowledge, and the importance of networks to model this. Ways to characterize biological networks will be presented as well as several modelling techniques. AI data-driven modelling techniques to infer biological network models will be explained, as well as how modelling techniques can be used to integrate different biological networks. Lastly, various modelling techniques on how to make biological prediction using the different biological networks are presented.
Q4 - Machine Learning in Bioinformatics
Learning from patterns in molecular biology data plays an important role in diagnosing disease, discovering new targets for therapy, and more generally in answering biological questions that lead to an improved understanding of biological systems with relevance to human health, industry, biotechnology, and agriculture.
This course focuses on methodology for the analysis of high-dimensional data in molecular biology, naturally addressing challenges that arise in the field such as learning from unlabelled data or from small numbers of samples. The methodology is introduced in the context of real-world applications with examples using real data.