AE9110 Bioinformatics and Biostatistics

    • Course code
      AE9110
    • Number of credits
      5
    • Teaching semester
      2025 Autumn
    • Language of instruction
      English
    • Campus
      Evenstad
    • Required prerequisite knowledge

      No special requirements.

      The course is a PhD-level course. National and international students admitted to a PhD program, or others fulfilling the requirement for admission to the PhD program may apply for admission to the course

Course content
  • Big data challenges including data capturing, storage, analysis, sharing, visualization, and information privacy
  • Key concepts and methods in bioinformatics including major research topics like data mining, molecular phylogenetics and functional analysis of biological data
  • Hierarchical models and Bayesian inference
  • Maximum Likelihood Estimation, information theory
  • General concepts, differences with the conventional approach,
  • Fitting and understanding regression models in the Bayesian framework
  • Fixed / random effects
  • Longitudinal, clustered, nested data
  • Flexibility of block-building hierarchical models

Learning Outcome

After completing the course, the students should have the following learning outcomes with regard to knowledge, skills and general competence:

 

Knowledge

The candidate:

  • knows the forefront of statistical methods used in research related to applied ecology or biotechnology
  • can independently evaluate how different statistical analysing methods fits to different study designs
  • has knowledge of tools and methods in the field of bioinformatics
Skills

The candidate:

  • can plan and carry out the analysing procedures in research and development work within applied ecology or biotechnology at high international standards
  • can interpret advanced statistical methods, such as data analysis with hierarchical models
  • has a thorough understanding regarding the usage of bioinformatics tools and methods associated with analysis and mining of big data
General competence

The candidate:

  • can carry out research with scholarly integrity
  • can make informed decision on which statistical approach that will be most suitable to address to complex scientific assignments
  • can participate in professional debates which depend on complex biostatistical understanding
  • can employ bioinformatics tools in their research work
Teaching and working methods

Lectures and computer lab.

2 weeks intensive course

Required coursework

Participation in 80% of the organised teaching

Form of assessment

One individual written report of an assigned biological problem. Graded as passed or failed.

Reading list

Faculty
Faculty of Applied Ecology, Agricultural Sciences and Biotechnology
Area of study
Matematisk-naturvitenskapelige fag/informatikk
Programme of study
PhD in Applied Ecology and biotechnology
Course level
Doctoral degree level (900-FU)