KIUA2010 Big Data Analytics in Life Sciences
- Course codeKIUA2010
- Number of credits10
- Teaching semester2026 Autumn
- Language of instructionNorwegian
- CampusHamar
- Required prerequisite knowledge
KIUA2006 Use of AI and ML in biochemistry and KIUA2007 Applied Bioinformatics for Sequence Data Analysis
Life sciences refer to the schools of science that study the structure and function of living organisms. Life sciences often have a specific focus on biological processes and phenomena relating to humans, animals, microorganisms and plants. Life sciences is an interdisciplinary field of research in which expertise from different fields is drawn on to better understand humans, as well as the ecological interaction between humans and nature (reference and more information: https://snl.no/livsvitenskap).
Life sciences lead to new solutions for the health sector (animal and human), as well as value creation for the Norwegian economy, which is able to develop innovative new products, services and jobs.
Big data has become an important element in many areas of life sciences. Here, emphasis is placed on the use of computer science, as most of the tasks related to biological data analysis are highly repetitive or mathematically complex. The use of data mining is important in the analysis of data from sequencing, images from microscopes and other imaging platforms, phenotypical data, clinical data for the collection of data and knowledge development.
This course delves deeper into the intersection between artificial intelligence (AI) and life sciences. The course is designed for students with a background in AI and ML and provides an introduction to life sciences, enabling students to build a bridge between the two fields. The course covers the following topics:
- Introduction to the different data types in life sciences
- Integration of variable data types for generation of useful information
- Use of AI and ML in life science fields
- ML methods for genotype-phenotype correlation
- Methods for analysing clinical human data
- Computer-aided drug and vaccine development
- Data security and privacy in life sciences
- Examples from ongoing projects from human, animal and environmental research
Learning Outcome
Upon successfully passing the course, students will have achieved the following learning outcomes:
The student will have
- extensive knowledge of fundamental concepts in life sciences and is able to provide an overview of the most important methods and tools used in the field
- knowledge of the different data types in life sciences, including omics, images, phenotypical, clinical, etc.
- fundamental knowledge of how to create different ML systems using existing ML libraries
- fundamental knowledge in evaluating the results of an ML system that is of relevance to life science data
- fundamental knowledge in detecting and avoiding over-fitting and batch effects
The student will be able to
- analyse problems in life sciences, define data collection requirements and design ML experiments to test hypotheses
- present ML results in scientific reports and identify key findings
- understand the underlying processes and mechanisms in ML algorithms and their limitations in the context of biological data analysis
- use techniques to integrate different data sources (omics, images, phenotypical, clinical, etc.) and gain holistic insights into complex biological systems
The student will be able to
- plan and conduct data analysis from different data types in life sciences (e.g. sequencing, images, clinical, etc.)
- ask the right biological questions and interpret data that is useful for the end user
- communicate key subject matter orally and in writing in Norwegian
- apply critical thinking and reflections around the opportunities and limitations that can be found in the field
- discuss ethical issues related to data security in life sciences, including privacy and responsible research practices
The course comprises a combination of lectures, group work, laboratory exercises, independent study and academic supervision.
- group presentations in accordance with the course curriculum
- participation in teaching and laboratory exercises in accordance with the course curriculum
Compulsory coursework requirements that have been passed are valid for 12 months only. Students wishing to take examinations after 12 months must pass the compulsory coursework requirements again in connection with the next scheduled delivery of the course.
| Form of assessment | Grading scale | Grouping | Duration of assessment | Support materials | Proportion | Comment |
|---|---|---|---|---|---|---|
Written examination with an adjusting oral examination | ECTS - A-F | Group | 4 Hour(s) |
| 100 |
- 4-hour invigilated individual written examination
- oral presentation that can adjust the final result one grade up or down
When taking part in group examinations, all participants in the group are responsible for all content of the task/product/work.
The assignment is assessed using a grading scale from A-F, where E is the lowest passing grade.
Students are able to choose which language to use for their examination. The available options are Norwegian Bokmål, Nynorsk and English.
Permitted aids:
- None