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

On completion of the course, the participants should be able to:

Knowledge

• Identify and describe different types of big data including molecular and registry data
• Describe the procedure for applying for data access, capturing data and how data harmonization is necessary for data analysis
• Discuss machine learning methods and provide examples of specific methods and their advantages and disadvantages

Skills

• Use data analysis programs such as R/RStudio
• Locate and apply for data access to registries
• Apply machine learning algorithms on big data to predict relevant outcomes
• Apply text mining 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 analyzing 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