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

Course content

To exploit the full potential of big data for translational medicine, analytic techniques and machine learning methods are crucial. This course will include subjects like getting access to, harmonizing, and analyzing big data as well as concrete examples of how big data is used in translational medicine. It will include classical machine learning, deep learning, and text mining methods with both lectures and practical computer exercises.

Education

BRIDGE - Translational Excellence Programme

A two-year postdoctoral fellowship in translational medicine

Learning outcome

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

 

Lectures and web-based and non-web-based, hands-on computer exercises.

Course literature is published on Absalon.

Participants must meet the admission criteria in BRIDGE - Translational Excellence Programme

Oral
Continuous feedback during the course of the semester
ECTS
0 ECTS
Type of assessment
Continuous assessment
Course participation
Attendance and active participation
Aid
All aids allowed
Marking scale
passed/not passed
Censorship form
No external censorship
Criteria for exam assessment

Active contribution and course participation according to the BRIDGE Guidelines.

Part time Master and Diploma courses

  • Category
  • Hours
  • Lectures
  • 12
  • Theory exercises
  • 12
  • English
  • 24