Search for contacts, projects,
courses and publications

Bioinformatics

Description

COURSE OBJECTIVES

Knowledge of basic principles of genome analysis Knowledge of basic principles of protein sequence analysis Building of 3D structures of proteins Docking simulations of drug binding to molecular targets.

 

COURSE DESCRIPTION

We deal with the most commonly used algorithms in biological applications. The first part of the course is dedicated to data mining algorithms and other classification methods such as markov models and other learning methods. We explore how to use such algorithms to analyse large amount of data (e.g., genome analysis) and to build 3D structures of proteins (e.g., using homology modeling). The last part of the course is focused on molecular docking algorithms and high throughput screening protocols used in virtual screening calculations.

 

LEARNING METHODS

Problem-Based Learning and Team-Based Learning Teaching method: frontal lectures + tutorial sessions

 

EXAMINATION INFORMATION
The course will be evaluated through assignments and a final project

 

REFERENCES
Notes and slides from the course Suggested (not mandatory) book: Essential Bioinformatics by Jin Xiong

People

 

Limongelli V.

Course director

Additional information

Semester
Fall
Academic year
2021-2022
ECTS
6
Language
English
Education
Master of Science in Artificial Intelligence, Elective course, Lecture, 1st year
Master of Science in Artificial Intelligence, Elective course, Lecture, 2nd year
Master of Science in Computational Science, Elective course, 1st year
Master of Science in Computational Science, Foundation course, 2nd year
PhD programme of the Faculty of Informatics, Elective course, Lecture, 1st year (4 ECTS)
PhD programme of the Faculty of Informatics, Elective course, Lecture, 2nd year (4 ECTS)