790388 Bayesian data analysis in the life sciences

Type
Lecture and exercise
Semester hours
3
Lecturer (assistant)
Sykacek, Peter
Organisation
Offered in
Wintersemester 2024/25
Languages of instruction
Englisch
Content

Introduction to Bayesian inference with applications in the life sciences.
I Theory

1) Crash course in Machine Learning
2) Bayesian concepts - from data to models
3) Techniques for Bayesian inference with focus on variational Bayes in conditional exponential family graphical models.
4) Tools for Bayesian inference with a focus on the Python 3 library BayesPy
5) Bayesian models with applications to computational biology

II Practical applications
Based on the skills acquired in the theory part of the lecture, students will develop Bayesian graphical models to solve challenges in the life science domain. After an introduction to BayesPy, students will work on these challenges independently. A typical challenge could for example require analyzing a public data set for low level molecular biological leads and map these leads to high level representations like GO:biological process terms or implied pathways. The solutions to these mini projects will be developed in BayesPy. Students may work on their own or team up with one colleague to share work and credit. An important aspect in applied data analysis is to document all analysis steps and results. Course participants are to this end required to hand in a lab protocol which consists of BayesPy analysis scripts and results. Detailed working instructions will be handed out in the course of the lecture.
Previous knowledge expected

Students are advised to take the "Machine Learning and Pattern Recognition for Bioinformatics" lecture before attending this course. Knowledge of mathematics (linear algebra), statistics and elementary programming skills are essential.
Objective (expected results of study and acquired competences)

Participants will be able to analyze simple Bayesian models analytically. Successful students will in addition know how to construct and infer a wide spectrum of Bayesian models with BayesPy and know how to apply such models for data analysis in the life sciences. Completion of the lecture will furthermore provide participants with the necessary background to understand scientific literature which enables them to tailor more complex models and Bayesian inference methods to challenges which emerge in scientific and R&D settings.
You can find more details like the schedule or information about exams on the course-page in BOKUonline.