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BioStatsLab (a BSc Bioinformatics Replacement for MInf3)
Basic mandatory course, B.Sc. Bioinformatics, Saarland University.
| Prerequisites | Mathematics (MInf1+Minf2, especially some analysis and linear algebra); good programming skills |
| Credits | 9 ECTS credits |
| Required time | 4V+2Ü (4 hours of lectures, 2 hours of tutorials per week) |
| Language | German (Materials are in English; course language is German) |
| Registration | click on Registration in the menu header |
| Details | available after registration in the Course Management system |
| Times |
Lecture: Wednesday 08:30 - 10:00 and Friday 12:15 - 13:45. (Starts on Wed Oct 16, 2024) |
| Mode | lecture in presence in E2.1, room 0.01 |
| Link | https://cms.sic.saarland/biostatslab24 |
| Instructor | Prof. Dr. Sven Rahmann |
| Tutorials | M. Sc. Johanna Schmitz |
Target audience
This course is offered as a basic lecture in the B.Sc. Bioinformatics program as a replacement for Mathematics for Informaticians 3 (MInf3).
Thus it should be taken in the 3rd semester, after completing MInf1 and MInf2, as well as Programming 1 & 2.
You will need some programming skills to qualify for the exam. Best would be Python, but you can use a language of your choice.
Please do not waste your time by attempting this course without a solid basis in programming.
Topics
The following topics will be covered in the course; additional topics may be included, depending on time and current events.
Probability
- randomness
- uniform distributions on finite sets (Laplace spaces)
- elementary and advanced combinatorics
- finite, discrete and continuous probability spaces
- random variables
- discrete probability distributions and where they come from
- probability distributions and OOP, scipy.stats
- conditional probabilities
- Bayes’ Theorem, simple version
- continuous probability distributions
- a glimpse at measure theory
- posterior distributions
Statistics
- descriptive statistics
- moments of random variables (expectation, variance, …)
- parametric models
- statistical testing (frequentist view)
- statistical testing (Bayesian view)
- parameter estimation: moments, maximum likelihood
- parameter estimation in mixture models: EM algorithm
- regression (simple linear, logistic, robust, multiple)
- regularization and Bayesian view on estimation
- robust regression
- multiple regression
- logistic regression
Stochastic Processes
- stochastic processes
- models for random sequences
- Markov chains
- Markov processes: models of sequence evolution
- Hidden Markov Models and applications
- Probabilistic Arthimetic Automata (PAAs) and applications
- the Poisson process
- distribution of DNA Motif Occurrences: compound Poisson
- significance of pairwise sequence alignment
Applications in Bioinformatics
- tests for differential gene expression
- Bayesian view on differential gene expression
- high-dimensionality low-sample problem
- multiple testing