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, 7 March 2017
- Responsible department
- Biology Education Centre
Entry requirements
Alt. 1) 120 credits in the engineering programme in molecular biotechnology including Multivariate data analysis and experimental design. Alt. 2) 120 credits. Bioinformatics - starting course (can be read in parallel).
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 exercises.
Assessment
Written examination (3 credits), computer exercises (2 credits).