News
No lecture on FridayWritten on 30.10.24 by Sven Rahmann Please remember that Friday is a holiday, and there will be no BioStasLab lecture. However, we will still release a new exercise sheet. The next lecture is on Wednesday (06.11.), continuing the discussion on conditional probabilities and Bayes' Theorem.
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ÜbungsterminWritten on 21.10.24 by Johanna Schmitz Hallo, ab nächster Woche findet die Übung jeden Dienstag, 10h-12h in Raum E2.1 001 statt. Viele Grüße |
Material online verfügbarWritten on 16.10.24 by Sven Rahmann Das Material (Folien) der 1. Vorlesung ist nun online verfügbar. Wir haben auch schon vorab das 1. Aufgabenblatt hochgeladen, damit Sie sich einen Eindruck verschaffen können. Bitte füllt das nuudel aus zur Terminfindung für die Übung (alles unter Material im CMS zu finden). |
BioStatsLab (a BSc Bioinformatics Replacement for MInf3)
Basic mandatory course, B.Sc. Bioinformatics, Saarland University.
This course is not available for credit points to students of other programs! You are welcome to audit, but will not get ECTS.
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