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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); solid programming skills |
Credits | 9 ECTS credits |
Required time | 4V+2Ü (4 hours of lectures, 2 hours of tutorials per week) |
Language | English (although this is a basic course, it also serves as an additional prerequisite for international Master students, and is hence given in English!) |
Registration | click on Registration in the menu header |
Materials | Materials will be available after registration under Information > Materials |
Times |
Lecture: Wednesday 08:30 - 10:00 and Friday 12:15 - 13:45. Tutorials: TBA Office Hour: TBA |
Mode | lecture in presence in E2.1, room TBA |
Link | https://cms.sic.saarland/biostatslab25 |
Instructor | Prof. Dr. Sven Rahmann |
Tutorials | M. Sc. Inês Alves Ferreira |
Exam | Written exam (up to 3h) at the end of the semester. Requirements to participate in the exam: - No score requirements for the exercises (pointless since LLMs can solve all of these) - Presentation of your solved exercises before the tutorial group (as explained in the first lecture) |
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.
It should be taken in parallel to Bioinformatics 1 during the B.Sc. Bioinformatics program.
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 how they are derived
- probability distributions and OOP, scipy.stats
- conditional probabilities
- Bayes’ Theorem, simple version
- moments of random variables (expectation, variance, …)
- continuous probability distributions
- a glimpse at measure theory
- posterior distributions
Statistics
- descriptive statistics
- parametric models
- statistical testing (frequentist view)
- statistical testing (Bayesian view)
- parameter estimation: moments, maximum likelihood
- parameter estimation in mixture models: EM algorithm
- regularization and Bayesian view on estimation
- linear regression
- 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
Multi-dimensional analysis (compact, 1 week)
- partial / total differentiability
- high-dimensional optimization