Bioinformatics II 1200-2MON38L

The course focuses on the following selected topics of current bioinformatics:

1) Clustering methods: greedy, K-means and hierarchical
3) Machine learning methods and deep learning: neural networks, decission trees and other approaches
2) Hidden Markov Models and sequence profiles
4) Protein threading
5) Comparative modelling
6) Geometric hashing and related algorithms for biomacromolecular structure analysis

During the last, seventh lecture each student presents a selected research paper.

Main fields of studies for MISMaP

physics
computer science
biotechnology
biology
mathematics
chemistry

Type of course

elective monographs

Mode

Classroom

Prerequisites (description)

It's assumed student already possess basic knowledge of bioinformatics, can explain such topics as homology, sequence alignment, phylogenetic trees, etc.

Course coordinators

Dominik Gront

Learning outcomes

Student:
- understands theoretical basis of selected bioinformatical methods
- 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

Additional information

Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system:

Description of 1200-2MON38L in USOSweb