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SK2538 Data-driven Life Sciences 7.5 credits

The future of life science is data-driven, providing major new opportunities to explore and understand biology, human health and changing ecosystems.

This course is offered to master's and PhD-level students.

For more information, please check our course website: https://ddls.aicell.io/

Choose semester and course offering

Choose semester and course offering to see current information and more about the course, such as course syllabus, study period, and application information.

Headings with content from the Course syllabus SK2538 (Autumn 2022–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

The course aims to introduce students to the field of computer-driven life sciences by letting them learn about their different application areas.

This course will introduce the student to data sets of different types, such as genomics, proteomics, metabolomics, transcriptomics, biomolecular structure, molecular dynamics simulations, imaging, video / audio recording, organism and habitat monitoring, population scale genetics, biobanks. Models of the biological phenomena and the related scientific breakthroughs based on the analysis of such data sets will be presented, analyzed and discussed.

Analysis techniques that will be introduced and used in this class belong to machine learning, artificial intelligence, other computational techniques for statistical analysis. In addition, visualization techniques will be introduced and discussed.

Another important aspect that will be introduced and discussed is related to ethics for data collection, management, analysis and sharing. The students will be specially trained in good practice related to computer-driven life sciences.

Intended learning outcomes

After passing the course, the student should be able to:

  • describe the field of data-driven life sciences, including an overview of the different application areas, and give examples of applications and their associated analysis methods
  • apply statistical analysis and machine learning analysis to biological data sets and formulate models of biological phenomena
  • present and review scientific literature in the field of computer-driven life sciences
  • reflect on ethical consequences of data-driven life sciences and describe good practice around the computer life cycle (collection, handling, sharing and analysis)

Course disposition

No information inserted

Literature and preparations

Specific prerequisites

Completed degree project at the undergraduate level and at least one completed course in biophysics, bioinformatics or equivalent.

English B / English 6

Recommended prerequisites

No information inserted

Equipment

No information inserted

Literature

No information inserted

Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

Grading scale

A, B, C, D, E, FX, F

Examination

  • LAB1 - Computer Lab, 2.0 credits, grading scale: P, F
  • PRO1 - Project, 3.0 credits, grading scale: P, F
  • TEN1 - Oral exam, 2.5 credits, grading scale: A, B, C, D, E, FX, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

Other requirements for final grade

Approved computer lab, project and oral exam. The grade on the exam determines the grade on the course.

Opportunity to complete the requirements via supplementary examination

No information inserted

Opportunity to raise an approved grade via renewed examination

Yes

Examiner

Ethical approach

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Further information

Course web

Further information about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.

Course web SK2538

Offered by

Main field of study

Engineering Physics

Education cycle

Second cycle

Add-on studies

No information inserted

Contact

Wei Ouyang (weio@kth.se)