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





Analysis of High Dimensional Omics Data DL (3781)
Coordinating lecturer:Prof. dr. Ziv SHKEDY 
Member of the teaching team:dr. Rahmasari Nur AZIZAH 

Credits: 3,0
Study load hours: 81
Period: semester 1 (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

Microarray technology and High dimensional omics datasets, Data processing, Asymptotic versus resampeling based inference, Statistical analysis of high dimensional data, group comparisons, Multiplicity, Exploratory versus Supervise analysis, Class prediction and Classification, Statistical modeling of microarray data, linear mixed models and their use in the high dimensional data setting


Compulsory course material
 

Handouts
 

Recommended reading
  Exploration and Analysis of DNA Microarray and Protein Array Data,Dhammika Amaratunga; Javier Cabrera and Ziv Shkedy,Wiley,9780471273981,Available as e-book: https://ebookcentral.proquest.com/lib/ubhasselt/detail.action?docID=1602 916&pq-origsite=summon


Organisational and teaching methods
Organisational methods  
Distance learning ✔  
Project ✔  


Evaluation

Semester 1 (3,00sp)

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



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

    • The student can critically appraise methodology and challenge proposals for and reported results of data analysis.

  •  EC 

    • The student can put research and consulting aspects of one or more statistical fields into practice.

  •  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.

  •  EC 

    • The student is capable of acquiring new knowledge.

 
Included in these programmesTolerance3
second year Data Science - distance learning Y
second year Master Bioinformatics - distance learning N
second year Master Biostatistics - distance learning Y
second year Quantitative Epidemiology - 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.