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

Course content

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.

Education

BRIDGE - Translational Excellence Programme

Learning outcome

Upon completing the course, participants should be able to:
 

Knowledge

  • 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.

 

Skills

  • 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.

 

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.

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.

Oral
Continuous feedback during the course of the semester
ECTS
0 ECTS
Type of assessment
Continuous assessment
Course participation
Type of assessment details
Attendance and active participation
Exam registration requirements

Participants are automatically registered for the examination upon admission to the BRIDGE - Translational Excellence Programme.

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 and Practicalities.

Part time Master and Diploma courses

  • Category
  • Hours
  • Lectures
  • 10
  • Preparation
  • 6
  • Theory exercises
  • 11
  • Excursions
  • 3
  • English
  • 30

Kursusinformation

Language
English
Course number
SBRI19004U
ECTS
0 ECTS
Programme level
Part Time Master
Ph.D.
Placement
Autumn
Schedulegroup
See course dates and course programme in Absalon.
Capacity
15 participants
Studyboard
Study Board for the Professionel Master´s Degree Programmes at The Faculty og Health and Medical Science
Contracting department
  • Center for Protein Research
Contracting faculty
  • Faculty of Health and Medical Sciences
Course Coordinators
  • Søren Brunak   (12-77737669723266767972656f44677476326f7932686f)
  • Isabella Friis Jørgensen   (18-6b756364676e6e63306c717469677075677042657274306d7730666d)
  • Sedrah Butt Balaganeshan   (6-75676674636a42657274306d7730666d)
Saved on the 05-07-2023

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Courseinformation of students