Big Data Analytics and Machine Learning – Computational Biology in Translational Medicine

Upon completing the course, participants should be able to:
 

Knowledge

• Identify and describe different types of big data including molecular (OMICS) and disease registry data.
• Describe what programming is useful for and why it is needed when working with big data.
• Discuss classical machine learning and deep learning methods and provide examples of specific methods and their advantages and disadvantages as well as discuss some use cases of machine learning of relevance in a clinical context.
• Acquire a basic understanding of neural network methods.

Skills

• The participants will be divided into beginners and intermediate/advanced learners regarding the data analysis program R/RStudio and will be introduced and taught accordingly.
• Demonstrate the potential of machine learning algorithms on big data
• Understand how text mining can be used for extracting information from clinical notes or biomedical literature.

Competences

• Discuss big data types and assess what such data can be used for in the context of translational medicine with specific focus on precision medicine.
• Benchmark and critically evaluate results of classical machine learning, deep learning and text mining methods for analysing big data.
• Reflect on the central aspects of big data analytics and be able to discuss and communicate to other scientists, clinicians, and the public.