The course consists of 14 lectures, 7 computer laboratory exercises (4 hours each), and 7 seminars.
FSK3538 Data-driven Life Sciences 7.5 credits
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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/
About course offering
For course offering
Autumn 2024 Start 26 Aug 2024 programme students
Target group
No information insertedPart of programme
No information insertedPeriods
P1 (7.5 hp)Duration
Pace of study
50%
Form of study
Normal Daytime
Language of instruction
English
Course location
KTH Solna
Number of places
Places are not limited
Planned modular schedule
Course memo
Course memo is not publishedSchedule
Schedule is not publishedApplication
For course offering
Autumn 2024 Start 26 Aug 2024 programme students
Application code
51058
Contact
For course offering
Autumn 2024 Start 26 Aug 2024 programme students
Contact
Wei Ouyang (weio@kth.se)
Examiner
No information insertedCourse coordinator
No information insertedTeachers
No information insertedContent 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.
Recommended prerequisites
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.