Big Data Analytics and Machine Learning – Computational Biology in Translational Medicine
To explore the full potential of big data for translational medicine, analytic techniques and machine learning methods are crucial. This course will include topics like data harmonisation, and alternative ways to analyse big data as well as concrete examples of how big data are used in translational medicine.
The course will introduce the participants to classical machine learning, deep learning, artificial intelligence, and text mining methods with both lectures and practical computer exercises. The course has speakers from the clinic and/or life science industry as well as top-level researchers within modern teachnologies.
BRIDGE - Translational Excellence Programme
Upon completing the course, participants should be able to:
- Identify and describe different types of big data including molecular 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.
- Get familiar with data analysis programs such as R/RStudio.
- Demonstrate the potential of machine learning algorithms on big data
- Using 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.
The course is organized with a mix of scientific seminars by
invited speakers from the clinic and/or life science industry,
including technical lectures about modern technologies, and
participant-led activities. In addition, the course will include
group work, practical computer exercises, and an excursion to a
pharmaceutical company. Scientific discussions within the teaching
sessions about the potentials of transfer learning and its use
within the participant’s respective research areas.
The course will end with an evaluation where participants must reflect on course learning outcomes and give feedback for course development.
Course literature will be published on Absalon.
Participants must meet the admission criteria in BRIDGE - Translational Excellence Programme.
The BRIDGE – Translational Excellence Programme offers a few selected 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 admission to the programme.
For further information about the programme, please go to the BRIDGE website: www.bridge.ku.dk
- 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 and Practicalities.
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
- Center for Protein Research
- Faculty of Health and Medical Sciences
- Søren Brunak (12-7975786b743468787b7467714669767834717b346a71)
- Isabella Friis Jørgensen (18-6c766465686f6f64316d72756a687176687143667375316e7831676e)
- Sedrah Butt Balaganeshan (6-7c6e6d7b6a71496c797b37747e376d74)
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Courseinformation of students