De elektronische studiegids voor het academiejaar 2026 - 2027 is onder voorbehoud.





Statistical and Computational Methods for Integrated Analysis DL (3790)
Coordinating lecturer:Prof. dr. Ziv SHKEDY 
Co-lecturer:Prof. dr. Willem TALLOEN 

Credits: 3,0
Study load hours: 81
Period: N (3sp)

Language of instruction: English
Exam contract: not possible

2nd Chance Exam1: Yes
Final grade2: Numerical
Tolerance3: See included in these programmes
Sequentiality
Mandatory sequentiality bound on the level of programme components
 
 
  Following programme components must have been included in your study programme in a previous education period
    Concepts of Bioinformatics DL (3584) 4.0 stptn  
    Concepts of Probability and Statistics DL (3220) 5.0 stptn  
    Linear Models DL (3577) 5.0 stptn  
 


Content

Asymptotic versus resampeling based inference. Permutations and bootstrap tests, resampeling methods, multiplicity, familywise error rate, false discovery rates, integrated analysis, and analysis of high dimensional data. Software development for high dimensional data


Compulsory course material
 

Handouts and papers.
 

Recommended reading
  Modeling Dose Response Microarray Data in Early Drug Development Experiments Using R: Order Restricted Analysis of Microarray Data,Lin D.; Shkedy Z.; Yekutieli D.; Amaratunga D.; Bijnens L.,1,Springer Verlag Berlin Heidelberg,9783642240065,Available as e-book: https://link.springer.com/book/10.1007%2F978-3-642-24007-2

Expl oration and Analysis of DNA Microarray and Protein Array Data,Dhammika Amaratunga; Javier Cabrera,Wiley,9780471273981


Organisational and teaching methods
Organisational methods  
Distance learning ✔  
Project ✔  


Evaluation

N (3,00sp)

Evaluation method
Oral exam50 %
Open questions✔
Presentation✔
Other exam50 %
Other Written project

Second examination period

Evaluation second examination opportunity different from first examination opprt
No


Learning outcomes
  EC = learning outcomes      DC = partial outcomes      BC = evaluation criteria  
Master of Statistics and Data Science
  •  EC 

    • The student can work in a multidisciplinary, intercultural, and international team.

  •  EC 

    • The student is able to correctly use the theory, either methodologically or in an application context or both, thus contributing to scientific research within the field of statistical science, data science, or within the field of application.

  •  EC 

    • The student is an effective written and oral communicator, both within their own field as well as across disciplines.

     
  •  DC 

    • The student is an effective writer in their own field.

  •  EC 

    • The student is capable of acquiring new knowledge.

 
Included in these programmesTolerance3
second year Master Bioinformatics - distance learning Y



1   Education, Examination and Legal Position Regulations art.12.2, section 2.
2   Education, Examination and Legal Position Regulations art.15.1, section 3.
3   Education, Examination and Legal Position Regulations art.16.9, section 2.