Table of Contents
Course code:
FSK3538(PhD): PhD student sign up here.
Welcome to data driven life sciences!
We are glad to welcome you.
The course aims to introduce you to the field of computer-driven life sciences by letting you learn about their different application areas.
This course introduces data sets of different types, such as proteomics, transcriptomics, biomolecular structure, molecular dynamics simulations and different types of imaging, and presents, analyzes and discusses models of biological phenomena and related scientific breakthroughs based on the analysis of such data sets.
Course goals (Intended Learning Outcomes)
We hope that you, by the end of this course, will 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 design
The course is divided into 6 modules, each module running for a week. Each week contains 3 main activities: a lecture on Tuesday, giving background information about the topic(s) of the week; a computer lab, usually on Wednesday, where you will practically explore data exploration using Python in Jupyter notebooks; and a seminar on Friday where you will collectively explain and discuss an assigned research paper.
Both the computer lab and the seminars are mandatory and graded activities!
For the labs, you be given a Jupyter notebook with tutorials and coding exercises. You will be running and practicing coding by running the Jupyter notebook in Google Colab. During the lab session, you can discuss your answers with the lab teachers. You will submit your final notebook with answers to the questions to be graded.
For the seminar, you will have a list of questions (the same every week) which we will answer during the seminar. Your participation and answer to these questions will be graded during the seminar.
In addition, in the examination week, there is a project to be conducted in pairs (Ms students) or alone (PhD students), the project will be graded by peers and the examiner.
Here are the published modules for the course:
Schedule
Important dates Course start: 29 August 2023 Course registration deadline: 4 September 2023 Course end: 6 October 2023 Exam: 11 October 2023 & 25 October 2023 NOTE: The schedule is subject to change, please keep an eye on the email announcement and the updated schedule HERE (which may differ from the schedule at KTH course web).
Prerequisites
Be prepared As prerequisites for the course, we recommend that you have a look at the following resources: Please have a look at the SciLifeLab Data-Driven Life Science (DDLS) initiative website to understand what data-driven life sciences are, and how Sweden is investing in this area.
Module 1
We will introduce the concept of data-driven life sciences and the data life cycle, the course and the different modules, and some basics about using ChatGPT. Lecture by Wei Ouyang on 29th August.
Module 2
We will introduce the basics of generative AI and its potential in life science. We will also cover recent trend of using generative large-language models and practical skills on how to use ChatGPT for reading, writing, planning and code generation. Lecture by Wei Ouyang (DDLS Fellow) on 5th Sept.
Module 3
Lecture by Juliette Griffié (DDLS Fellow) on 12th Sept, at 08:00-10:00.
Module 4
We will join the Computational Methods in Evolution and Biodiversity workshop in Stockholm University, participate in lectures by Serge Belongie, Katie E. Lotterhos, Tobias Andermann (DDLS Fellow) et al. on 21st Sept. The computer lab will happen on the same day in the afternoon.
Module 5
Lecture by Wen Zhong (DDLS Fellow) and Estibaliz Gómez de Mariscal (PhD)
Module 6
Lecture by Darko Mitrovic (PhD) on 3rd Oct.
Final Project
Final project plan and report for the DDLS course.
(The list of modules will be updated as the course progresses.)
Each module will contain quiz or assignment, please pay attention to the deadline.
Grades
The final grade on the course is determined as follows:
The computer labs are graded P/F. To pass, you need to attend all the labs and answer the questions to a satisfactory degree.
Participation in the seminars is graded as P/F. To pass, you need to read the assigned papers, attend all the seminars and participate in discussions.
The project is graded P/F. To pass, you need to carry out the project, and get a passing grade on the project report.
The oral exam (project presentation) at the end of course will be graded A-F scale.
For master students, the grade on the oral exam determines the final grade on the course provided the other three activities have received a passing grade.
For PhD students, the oral exam is not mandatory, but you are welcome to join the oral exam if you want to practice your presentation skills.
The submission of the computer lab, the project report and the oral exam will be first reviewed and graded by your peers, and then the final grade and feedbacks will be given by the teachers and examiner.
Communication and groups
You can find all announcements at here.
For questions, please email the course responsible, see contact here.
Meet your instructor
In this course, you will meet:
- Wei Ouyang, assistant professor in biophysics who will be holding the lectures, seminars and grading them (weio@kth.se). Wei is also the course responsible.
- Antoni Marciniak, PhD student in biophysics, who will be holding and grading the computer labs (awma@kth.se)
- Darko Mitrovic, PhD student in biophysics, who will join us in module 6, for giving lecture, holding and grading the computer labs (darmi@kth.se)
In various modules, we will also have guest lectures and labs by
- Juliette Griffié: Stockholm University, SciLifeLab DDLS fellow(juliette.griffie@dbb.su.se)
- Wen Zhong: Linköpings universitet, SciLifeLab DDLS fellow (wen.zhong@scilifelab.se)
- Tobias Andermann: Uppsala university, SciLifeLab DDLS fellow (tobias.andermann@ebc.uu.se)
- Estibaliz Gómez de Mariscal: the Instituto Gulbenkian de Ciência, Portugal (egomez@igc.gulbenkian.pt)
Course schedule
See the course schedule for the detailed schedule.
FAQs
Are there prerequisites?
The students are expected to have basic knowledge of biology and programming in Python. If you are not familiar with Python, it will be helpful if you can go through the prerequisites.
How often do the courses run?
We run the course once a year, in Period 1 from August to October.
How do I register the course?
- For master students, please register at the KTH course selection system.
- For PhD students, please sign up at here.
How do I get access to the course material?
The course material are available at here. We won’t be using KTH Canvas.
Can I attend the course remotely?
It is possible only for the lectures. Except the first lecture (in person), all other lectures will be given over Zoom (zoom link: https://kth-se.zoom.us/j/69812177998).
The computer labs and seminars are provided in person. Exceptions will be announced in the email and the updated schedule above.
How do I get access to the computer labs?
The computer labs are normally provided in person (exception may apply, check email announcement and the updated schedule). If you are not able to attend, please contact the course responsible.
How do I get access to the seminars?
The seminars are normally provided in person (exception may apply, check email announcement and the updated schedule). If you are not able to attend, please contact the course responsible.
What if I cannot attend the computer labs or seminars?
It is mandatory to attend all the computer labs or seminars, if you anticipate that you will miss a computer lab or seminar, please notify the course responsible as soon as possible, exceptions will be made on a case-by-case basis.
If the exception is granted, you will still need to submit the notebook for the computer lab, or write a report to answer the seminar question sheet.
Will the lecture be recorded?
The lecture will not be recorded, but the slides will be made available after the lecture.
Can I use ChatGPT / Claude or other generative AI tools in the computer labs, seminar, assignments or the final project?
Yes, you are encouraged to use these AI tools to facilitate your learning, and perform tasks. However, you need to make sure that you are using them in a responsible way. Importantly, if you used it for submitting any graded activities, please make sure to also attach the exported conversation history with the AI tool.