Statistical genomics (19/20)
Faculteit  Science and Engineering 
Jaar  2019/20 
Vakcode  WISG09 
Vaknaam  Statistical genomics (19/20) 
Niveau(s)  master 
Voertaal  Engels 
Periode  semester I a 
ECTS  5 
Rooster  rooster.rug.nl 
Uitgebreide vaknaam  Statistical Genomics (tweejaarlijks 2019/2020)  
Leerdoelen  At the end of the course, the student is able to: 1. translate applied and realworld problems into graphical models 2. interpret a graphical model w.r.t. its conditional independencyproperties 3. statistically model graphs 4. design MCMC sampling algorithms for graphs 5. infer graphical models from data 6. critically assess inference algorithms for graphical models 

Omschrijving  In the course we will mainly focus on directed graphical models (Bayesian networks), which can for example be used to infer gene regulatory networks and protein pathways in systems biology research. After a very brief introduction to these biological applications, we will consider Bayesian networks from a mathematical perspective. As Bayesian networks can effectively be seen as a marriage of graph theory and probability theory, we will first discuss various graph theoretic concepts, before we can start modelling graphs statistically. In this context we will have to briefly discuss (or repeat respectively) some fundamentals of Bayesian Statistics. Finally, we will learn how to learn/infer Bayesian networks from data. Throughout the course we will use the statistical computing environment R to implement some of the discussed algorithms, while other more sophisticated implementations will be made available. More specifically, the topics, covered in this course, include: 1. An introduction to graphical models in Statistical Genomics 2. An introduction to the general graphical model terminologies 3. The Markov property, conditional independency relations, and the concept of dseparation 4. Equivalence classes of graphs (CPDAGs) and singleedge operations 5. The BDe scoring metric for discrete Bayesian networks 6. The BGe scoring metric for Gaussian Bayesian networks 7. Graph inference with Greedy Search algorithms (for finding ‘the best’ graph) 8. Graph inference with Markov Chain Monte Carlo (MCMC) simulations (for model averaging) 9. Dynamic Bayesian networks for time series data 10. Other graphical models, advanced Bayesian network models and improved MCMC sampling schemes 

Uren per week  
Onderwijsvorm 
Hoorcollege (LC), Opdracht (ASM), Practisch werk (PRC)
(4 hours lectures and 2 hours tutorials (partly in a computer lab) per week) 

Toetsvorm 
Opdracht (AST), Verslag (R)
(Assessment takes place through three homework assignments and a final research project report (RPR) according to the formula: Final = 0.1 x max(HW1, RPR) + 0.1 x max(HW2, RPR) + 0.1 x max(HW3, RPR) + 0.7 x RPR where HW1, HW2 and HW3 are the homework grades for 1st , 2nd, and 3rd homework and RPR is the research project report grade.) 

Vaksoort  master  
Coördinator  dr. M.A. Grzegorczyk  
Docent(en)  dr. M.A. Grzegorczyk  
Verplichte literatuur 


Entreevoorwaarden  The course unit assumes prior knowledge acquired from the two course units ‘Probability Theory’ and ‘Statistics’ of the Mathematics BSc Programme.  
Opmerkingen  
Opgenomen in 
