Capita Selecta of Computational Biology (3772)
  
Coordinating lecturer :Prof. dr. Niel HENS 
  
Co-lecturer :dr. Tarylee REDDY 
  
Member of the teaching team :dr. Jade MEMBREBE 
 dr. Leyla KODALCI 

Language of instruction : English
Credits: 3,0
  
Period: semester 1 (3sp)
  
2nd Chance Exam1: Yes
  
Final grade2: Numerical
 
Exam contract: not possible
 
Sequentiality
 
   No sequentiality

Prerequisites

The student needs to havebasic knowledge of fundamental statistical concepts.


Content

Selected topics in computational biology are covered in this course each academic year.

For AY 2023-2024, the first part of the course will cover “Phylogenetic inference and itsapplications” and the second part of course will cover “Approaches for Handling MissingValues in the Analysis of Gene Expression Data Evaluation”.

This course is organized in cooperation with lecturers from our South partners.


Organisational and teaching methods
Organisational methods  
Lecture ✔  
Project ✔  
Teaching methods  
Paper ✔  
Presentation ✔  


Evaluation

Semester 1 (3,00sp)

Evaluation method
Written evaluation during teaching period75 %
Transfer of partial marks within the academic year✔
Homework✔
Paper✔
Oral exam25 %
Open questions✔
Evaluation conditions (participation and/or pass)✔
Conditions The student needs to participate in all the components of the evaluation.
Consequences If the above condition is not met, the final mark will by the minimum of: - 9 - the total score of all evaluation components.

Second examination period

Evaluation second examination opportunity different from first examination opprt
No
 

Compulsory course material
 

Handouts made available by the instructors on Blackboard.


Learning outcomes
Master of Statistics and Data Science
  •  EC 

    • The student can handle scientific quantitative research questions, independently, effectively, creatively, and correctly using state-of-the-art design and analysis methodology and software.

     
  •  DC 

    • ... correctly using state-of-the-art analysis methodology.

     
  •  DC 

    • ... correctly using state-of-the-art software.

  •  EC 

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

  •  EC 

    • The student has the habit to assess data quality and integrity. 

  •  EC 

    • The student is capable of acquiring new knowledge.

  •  EC 

    • The student knows the societal relevance of statistics and data science.

 
  EC = learning outcomes      DC = partial outcomes      BC = evaluation criteria  
Offered inTolerance3
2nd year Master Bioinformatics J
2nd year Master Bioinformatics - icp J
2nd year Master Biostatistics J
2nd year Master Biostatistics - icp J
2nd year Master Quantitative Epidemiology J
2nd year Master Quantitative Epidemiology - icp J
Exchange Programme Statistics J



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.