Conducted in
term:
2023L
Erasmus code: 13.3
ISCED code: 0531
ECTS credits:
1.5
Language:
English
Organized by:
Faculty of Chemistry
Bioinformatics II 1200-2EN-MON15L
The course focuses on the following selected topics of current bioinformatics:
1) Clustering methods: greedy, K-means, hierarchical
3) Machine learning, deep learning
2) Hidden Markov Models, sequence profiles
4) Protein threading
5) Comparative modelling
6) Geometric hashing and related algorithms for biomacromolecular structure analysis
Course coordinators
Type of course
elective monographs
Prerequisites (description)
It's assumed student already possess basic knowledge of bioinformatics, can explain such topics as homology, sequence alignment, phylogenetic trees, etc.
Learning outcomes
Student:
- understands theoretical basis of a bioinformatical method
- can describe its algorithm
- can apply a tool appropriately to a given research problem
Assessment criteria
Oral presentation of a selected research publication relevant to the course. Student must be present on every lecture
Practical placement
N/A
Bibliography
Review articles circulated during the course