The course consists of 14 lectures, 7 computer laboratory exercises (4 hours each), and 7 seminars.
FSK3538 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.
- Master's: https://www.kth.se/student/kurser/kurs/SK2538
- PhD: https://www.kth.se/student/kurser/kurs/FSK3538
For more information, please check our course website: https://ddls.aicell.io/
Information per course offering
Information for Autumn 2024 Start 26 Aug 2024 programme students
- Course location
KTH Solna
- Duration
- 26 Aug 2024 - 27 Oct 2024
- Periods
- P1 (7.5 hp)
- Pace of study
50%
- Application code
51058
- Form of study
Normal Daytime
- Language of instruction
English
- Course memo
- Course memo is not published
- Number of places
Places are not limited
- Target group
- No information inserted
- Planned modular schedule
- [object Object]
- Schedule
- Schedule is not published
- Part of programme
- No information inserted
Contact
Wei Ouyang (weio@kth.se)
Course syllabus as PDF
Please note: all information from the Course syllabus is available on this page in an accessible format.
Course syllabus FSK3538 (Spring 2022–)Content and learning outcomes
Course disposition
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)
Literature and preparations
Specific prerequisites
Enrolled PhD student.
Equipment
Literature
Examination and completion
If the course is discontinued, students may request to be examined during the following two academic years.
Grading scale
Examination
- LAB1 - Computer lab, 2.0 credits, grading scale: P, F
- PRO1 - Project, 5.5 credits, grading scale: P, 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.
Opportunity to complete the requirements via supplementary examination
Opportunity to raise an approved grade via renewed examination
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.