The Multi-omics Network Analytics group and the Data Science Platform at DTU Biosustain have come together to offer a diverse range of courses designed to empower students with essential data science skills and familiarize them with both internal and external tools we have developed.

We are strong advocates of open science, and as part of that commitment, we make sure to share all the course materials on our GitHub repository. This means that you will have access to comprehensive resources that aim to enhance the learning experience.

Our courses are designed typically following a short-training format, ranging from single lectures to a few days of intensive, hands-on instruction. Explore our course offerings and contribute or provide feedback!

Courses

Protein Language Models [link]

An up-to-date course at the intersection of language models and the complex world of proteins! This course provides an overview of the current state of the art of Protein Language Models (pML), which are revolutionizing research, discovery, and innovation in the field of molecular biology.

Objectives

• Gain a comprehensive understanding of the synergy between language models and protein data
• Equip students with the skills to leverage pre-trained models and fine-tuning for bioinformatics applications
• Explore cutting-edge use cases and discover how protein language models are shaping the future of molecular biology
• Understand the current limitations of the field and be prepared to tackle bioinformatics challenges

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Networks to Study Microbes [link]

The goal of the course is to provide an introduction to the use of networks in microbiology. The students will receive an overview of the field of computational microbiology with special emphasis on data standardization* and data sources, and how to use graph structures to solve biological questions. Further, the students will get a hands on introduction to Python programming language and its scientific libraries.

Objectives

• Use multiple data sources for studying microbes in different biological contexts
• Build networks, visualize and analyse them using different tools
• Use Python to extract, transform and analyse different data structures

NOTE: Some of the course content is in Spanish, but we are working on an english version. Coming out soon!

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Leveraging Existing Biological Data and Making Sense of it [link]

This course is designed to introduce students to the essential skills required to fully utilize available resources. Further, this course provides an overview of how networks can be used to integrate and analyse these data.

Objectives

• Understand the fundamental principles of open science and its impact on research and collaboration
• Explore the FAIR (Findable, Accessible, Interoperable, and Reusable) data principles to ensure your data is valuable and widely usable
• Dive into ontologies and structured data, and learn how to apply them effectively
• Discover diverse data sources and learn how to generate and find open data
• Explore the graphs and networks as powerful visual representation of complex data

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Introduction to Knowledge Graphs [link]

An introduction to the fascinating world of Knowledge Graphs and their vital role in the field of biology. This course explores fundamental concepts, practical applications, and significance of Knowledge Graphs in understanding complex biological systems. Whether you’re a biologist, data enthusiast, or simply curious about the intersection of knowledge representation and biology, this course is your gateway to how to leverage interconnected biological data.

Objectives

• Familiarize with graph-based representations and their applications in biology
• Understand the principles and structure of Knowledge Graphs, enabling you to work with diverse biological data
• Gain insight into data standards and graph databases in creating effective Knowledge Graphs
• Explore real-world examples to inspire innovative approaches to biological research
• Learn practical analysis techniques to harness the power of Knowledge Graphs for insightful discoveries in biology