Statistical Inference for Bioinformatics
Syllabus, Master's level, 1MB459
This course has been discontinued.
- Code
- 1MB459
- Education cycle
- Second cycle
- Main field(s) of study and in-depth level
- Bioinformatics A1N, Technology A1N
- Grading system
- Pass with distinction (5), Pass with credit (4), Pass (3), Fail (U)
- Finalised by
- The Faculty Board of Science and Technology, 21 October 2019
- Responsible department
- Biology Education Centre
Entry requirements
Alt. 1) 120 credits in the engineering programme in molecular biotechnology. Alt. 2) 120 credits including Introduction to Bioinformatics, and Introduction to Programming, Scientific Computing and Statistics. Alt. 3. 120 credits including 30 credits mathematics and 30 credits computer science, and Introduction to Bioinformatics.
Learning outcomes
After passing the course the student should be able to
- account for and apply classical statistical inference based on Bayesian and frequentist methods, traditional computer-based methods, as well as computer-intensive methods, for analysis one variable alone and several variables at the same time
- choose and apply the appropriate among above-mentioned methods and technologies for statistical inference for a given set of biological and biomedical molecular data and their associated biomedical questions.
Content
Classical inference: Frequentist and Bayesian inference, maximum likelihood estimation. Traditional computer-based methods: Empirical Bayes, ridge regression, generalized linear models, regression trees, survival analysis and the EM-algorithm. Computer-intensive methods as resampling, resampling based confidence intervals, cross validation, large-scale hypothesis testing, sparse regression models, random forests, and boosting. Bioinformatic application examples.
Instruction
Lectures, calculation exercises and computer labs.
Assessment
Written examination (3 credits), computer labs (2 credits).