Big Data Analytics and Machine Learning I – Computational Biology in Translational Medicine
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.
BRIDGE - Translational Excellence Programme
On completion of the course, the participants should be able to:
- 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
- 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
- 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.
The BRIDGE – Translational Excellence Programme offers a few select graduated PhDs a two-year postdoctoral fellowship in translational medicine. The courses are only available to the fellows enrolled in the programme. Fellows are automatically enrolled in the courses upon appointment in the programme.
For further information: https://bridge.ku.dk/about/
- 0 ECTS
- Type of assessment
Continuous assessmentCourse participation
- Type of assessment details
- Attendance and active participation.
- 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
- Theory exercises
- Course number
- 0 ECTS
- Programme level
- Part Time Master
See course dates and course programme in Absalon.
- 15 participants
- Study Board for the Professionel Master´s Degree Programmes at The Faculty og Health and Medical Science
- Department of Biomedical Sciences
- Faculty of Health and Medical Sciences
- Søren Brunak (12-7f7b7e717a3a6e7e817a6d774c6f7c7e3a77813a7077)
- Sedrah Butt Balaganeshan (6-796b6a78676e4669767834717b346a71)
- Isabella Friis Jørgensen (18-6f7967686b727267347075786d6b74796b744669767834717b346a71)
Are you BA- or KA-student?
Courseinformation of students