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

Course content

Big data is a crucial resource for translational medicine. Registries contain decades of priceless individual-level information about disease history, medicine use, family relationships etc. The development of high-throughput methods has also led to a drastic increase in big data available. Both data types are already used in the clinic for diagnosis, to predict treatment responses etc. Yet, we are only in the beginning of exploiting the potential of big data in translational medicine. One of the key tool areas for utilizing such data is through bioinformatics analysis.

The focus in bioinformatics is on integrative analysis of large data ranging from molecular-level data on sequences, gene transcription patterns and molecular interactions to clinical data. A major aim is to gain insights into both rare and complex diseases in relation to patient stratification and elucidation of differences in the biological mechanisms that lead to similar phenotypes.

The objectives of the course are to provide knowledge how to get data access to big data of various types and of a range of methods, including machine learning and biological network analysis, for finding, analyzing and integrating heterogeneous data in the context of a specific disease , and to provide students with the necessary foundation to critically evaluate results of such analyzes. The critical assessment is relevant in the context of decision support systems where elements of the learning healthcare system, benchmarking and general insights into evidence-based medicine are important.

Education

BRIDGE - Translational Excellence Program

 

Learning outcome

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

Knowledge

  • Identify and describe resources of big data including omics and registry data
  • Explain the procedure for applying for data access, capturing data and how data harmonization is necessary for data analysis and integration
  • Discuss how heterogeneous data can be integrated and evaluated
  • Explain machine learning and provide examples of specific methods and their advantages and disadvantages
  • Design studies and explain workflows for integrative analysis of big data for generation of disease knowledge

 

Skills

  • Use data analysis programs such as Unix / Linux
  • Locate and apply for data access to registries
  • Run machine learning algorithms on big data to predict patient mortality, identify disease biomarkers relevant patient outcome or similar
  • Apply text mining for extracting information from clinical notes or biomedical literature
  • Perform integrative analysis of heterogeneous data such as data and clinical data
  • Present heterogeneous data on a biological system for employing biological questions 

 

Competences

  • Overview of big data types and what such data can be used for in the context of translational medicine with specific focus on precision medicine
  • Master a range of methods for analyzing and integrating heterogeneous data in the context of a specific disease including machine learning methods and network analysis
  • Benchmark and critically evaluate results of such analyzes
  • Understand the central aspects of big data analytics and be able to discuss and communicate to other scientists, clinicians, and the public

5 full days with lectures and web-based and non-web-based, hands-on computer exercises.

To be added

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

i) an overview theory and application of technologies in epigenetic modification and genomic regulation,

ii) a basic overview of modern human tissue and organ models,

iii) an overview of possibilities of system biology approaches for integrating omics data in translational research,

iv) an understanding of application of technologies in microbiome research in health and disease, and v) knowledge of online omics resources and tools.

Part time Master and Diploma courses

  • Category
  • Hours
  • Lectures
  • 15
  • Theory exercises
  • 15
  • English
  • 30

Kursusinformation

Language
English
Course number
SBRI19004U
ECTS
0 ECTS
Programme level
Part Time Master
Ph.D.
Duration
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
  • Department of Biomedical Sciences
Contracting faculty
  • Faculty of Health and Medical Sciences
Course Coordinators
  • Søren Brunak   (12-7c787b6e77376b7b7e776a74496c797b37747e376d74)
  • Kirstine G Belling   (16-78767f8081767b723b6f727979767b744d707d7f3b78823b7178)
Saved on the 19-09-2019

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