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Bioinformatics II

General data

Course ID:	1200-3MON38L
Erasmus code / ISCED:	13.3 Kod klasyfikacyjny przedmiotu składa się z trzech do pięciu cyfr, przy czym trzy pierwsze oznaczają klasyfikację dziedziny wg. Listy kodów dziedzin obowiązującej w programie Socrates/Erasmus, czwarta (dotąd na ogół 0) – ewentualne uszczegółowienie informacji o dyscyplinie, piąta – stopień zaawansowania przedmiotu ustalony na podstawie roku studiów, dla którego przedmiot jest przeznaczony. / (0531) Chemistry The ISCED (International Standard Classification of Education) code has been designed by UNESCO.
Course title:	Bioinformatics II
Name in Polish:	Bioinformatyka II
Organizational unit:	Faculty of Chemistry
Course groups:	(in Polish) Wykłady monograficzne w semestrze letnim (Szkoła Doktorska)
ECTS credit allocation (and other scores):	1.50 Basic information on ECTS credits allocation principles: the annual hourly workload of the student’s work required to achieve the expected learning outcomes for a given stage is 1500-1800h, corresponding to 60 ECTS; the student’s weekly hourly workload is 45 h; 1 ECTS point corresponds to 25-30 hours of student work needed to achieve the assumed learning outcomes; weekly student workload necessary to achieve the assumed learning outcomes allows to obtain 1.5 ECTS; work required to pass the course, which has been assigned 3 ECTS, constitutes 10% of the semester student load.
Language:	Polish
Main fields of studies for MISMaP:	biology biotechnology chemistry computer science mathematics physics
Type of course:	elective courses
Prerequisites (description):	It's assumed student already possess basic knowledge of bioinformatics, can explain such topics as homology, sequence alignment, phylogenetic trees, etc.
Short description:	This course extends basic knowledge of bioinformatics in selected topics, relevant to the current research directions, such as protein structure prediction and analysis, analysis of metagenomic sequences etc.
Full description:	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.
Bibliography:	Review articles circulated during the course
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 methods and assessment criteria:	Oral presentation of a selected research publication relevant to the course. Student must be present on every lecture
Practical placement:	N/A

Classes in period "Summer semester 2023/24" (in progress)

Time span:	2024-02-19 - 2024-06-16	Selected timetable range: weekly course term Navigate to timetable
Type of class:	Monographic lecture, 15 hours, 10 places more information
Coordinators:	Dominik Gront
Group instructors:	Dominik Gront
Students list:	(inaccessible to you)
Examination:	Grading

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