Specific programme courses of 2nd stage Bioinformatics (course group defined by Faculty of Mathematics, Informatics, and Mechanics)
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2023Z - Winter semester 2023/24 2023L - Summer semester 2023/24 2024Z - Winter semester 2024/25 2024L - Summer semester 2024/25 (there could be semester, trimester or one-year classes) |
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| 1100-CM |
 
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Classes
Winter semester 2023/24
Groups
Brief description
No brief description found, go to course home page to get more information.
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| 1100-MMCSB2 | n/a |
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Classes
Summer semester 2023/24
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Brief description
The subject is devoted to the presentation of the research strategy of biomolecular systems using the methods of theoretical physics and various computer simulation techniques. In particular, issues related to computer-aided drug design will be discussed. Basic and advanced methods such as molecular docking, molecular dynamics, quantum-classical molecular dynamics, free energy computation methods and non-equilibrium molecular-dynamics-based methods will be discussed. During the laboratory, students will carry out three projects, covering the most common problems raised in research in the field of computational biophysics. |
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| 1000-2N00SID | n/a |
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Classes
Summer semester 2023/24
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Brief description
The course is focused on using intelligent methods for solving problems that are difficult or impractical to solve with other methods. Accordingly, we discuss, among the others, various approaches based on heuristics, approximations, randomized, as well as deductive and inductive schemes of reasoning, often designed by analogy to the human way of problem solving. The main topics include also intelligent search through large spaces of states and solutions, intelligent game strategies, reasoning in logic and logical foundations of planning, foundations of machine learning in relation to artificial intelligence, foundations of modeling of uncertainty, as well as various specialized applications. |
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| 1400-235CHiE | n/a |
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Classes
Summer semester 2023/24
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Brief description
Lectures present actual knowledge about chromatin biology. Among the subjects covered by the course are: chromatin structure, epigenetic modifications, chromatin-based regulatory mechanisms, cellular memory, and epigenetic inheritance. |
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| 1000-719DAV | n/a |
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Classes
Summer semester 2023/24
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Brief description
The aim of the course is to introduce the techniques of data analysis and visualization to the students. |
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| 1000-2M23DLS | n/a |
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Classes
Summer semester 2023/24
Groups
Brief description
No brief description found, go to course home page to get more information.
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| 1000-317bDNN |
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Classes
Winter semester 2023/24
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Brief description
The goal of the course is to show usage cases for deep neural networks. During the course state-of-the-art techniques, algorithms and tools will be presented. Among others two main blocks of the course will concern image classification and text processing. |
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| 1100-4BM21 | n/a |
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Classes
Summer semester 2023/24
Groups
Brief description
No brief description found, go to course home page to get more information.
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| 1000-720IPZ |
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Classes
Winter semester 2023/24
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Brief description
During the class the students work in 3-5 person teams and solve one of the two connected problems. Each year it is a different inter-disciplinary problem requiring some programming and data analysis skills. Then, in the second part of the semester, the teams are paired and required to integrate their solutions of two subproblems into a larger software, answering a bigger problem. The semester ends with presentations of all teams and a symposium with a jury comprised of international scientists. The course is held together with the Sorbonne university and the Heidelberg University. |
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| 1000-2N03BO | n/a |
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Classes
Summer semester 2023/24
Groups
Brief description
The aim of this course is to introduce students with a computer science and mathematics background to data-driven problems from molecular biology (we will focus on analysis of protein and nucleic acid sequences). In this course we will present some of the mathematical models and computational methods used today in molecular sequence analysis. |
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| 1100-2BB111 | n/a |
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Classes
Summer semester 2023/24
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Brief description
(in Polish) Przedmiot jest wstępem do mechaniki kwantowej układów molekularnych |
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| 1100-3BN17 |
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Classes
Winter semester 2023/24
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Brief description
Lecture and exercises introduce the students to the subject and methodology of machine learning and modeling of artificial neural networks, and to solving practical problems with these tools. The lecture is intended for third-year students of Neuroinformatics. |
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| 1000-135MBM | n/a |
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Classes
Summer semester 2023/24
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Brief description
The lecture is devoted to the widely understood mathematical modelling in biology and medicine. We mainly focus on ecological models which are built using differential and difference equations. We also consider models of immune reactions and those of classical genetics (Mendel theory) based on Markov chains. |
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| 1400-217MNiTP | n/a |
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Classes
Summer semester 2023/24
Groups
Brief description
Cancer epidemiology as well as environmental and endogenous factors, which favor formation of tumors. Mechanisms of induction of mutations as causative factor of carcinogenic process will be discussed, as well as epigenetic mechanisms (DNA methylation, miRNA). DNA repair mechanisms will be presented as well as tumor cell characteristics, stages of carcinogenesis, major critical genes, dysfuction of which triggers carcinogenic process (oncogenes, tumor suppressor genes), apoptosis, proteolysis, angiogenesis and metastasis. Cancer stem cells. Role of viruses in cancer induction. Diagnostics and the basis of modern treatment methods; personal therapy. |
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| 1400-228MiFM | n/a |
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Classes
Summer semester 2023/24
Groups
- Facultative courses, BIOLOGY, specialization level (2nd study cycle), spec.: BM (Faculty of Biology)
Brief description
(in Polish) Zajęcia mają za zadanie zaznajomienie studentów z podstawami filogenetyki molekularnej oraz metagenomiki (analiza amplikonów), ze szczególnym uwzględnieniem ich zastosowania w badaniach środowiskowych. W ramach bloku dotyczącego filogenetyki studenci nauczą się przyrównywania sekwencji nukleotydowych i białkowych, poznają podstawowe metody rekonstrukcji drzew filogenetycznych oraz oceny ich mocy, a także zapoznają się z podstawami datowania filogenezy oraz metodami szacowania stanów ancestralnych. A ramach drugiej części zajęć nacisk zostanie położony na analizę danych środowiskowych z sekwencjonowania nowej generacji (NGS). Podczas zajęć samodzielnie przeprowadzą analizę od etapu uzyskania surowych danych, poprzez analizę ich jakości, łączenie odczytów, oraz klasyfikację taksonomiczna sekwencji, kończąc na analizach statystycznych umożliwiających powiązanie czynników środowiskowych z otrzymanymi wynikami składu gatunkowego i wskaźnikami różnorodności biologicznej. |
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| 1400-216BKWN | n/a |
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Classes
Summer semester 2023/24
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Brief description
The objective of the course is to present methods used to study proteins. Students purify proteins from natural sources using conventional protocols and they learn novel isolation techniques used for purification of recombinant proteins. |
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| 1400-225PROT-en |
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Classes
Winter semester 2023/24
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Brief description
In this course students get acquainted with the proteomic methodology |
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| 1000-135PSB | n/a |
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Classes
Summer semester 2023/24
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Brief description
Lectures on theoretical foundations of stochastic analysis (Markov chains, Poisson process, birth and death processes, Master and Fokker-Planck equations will be integrated with concrete biological models on the micro level (gene expression and regulation, ion channels) and on the macro level (evolutionary game theory). |
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| 1000-135SST | n/a |
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Classes
Summer semester 2023/24
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Brief description
The course concerns computer simulation of random variables and simple stochastic processes. It comprises also an introduction to Monte Carlo (MC) methods, also known as randomized algorithms. |
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| 1000-318bVR | n/a |
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Classes
Summer semester 2023/24
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Brief description
The goal of the course is to present deep learning architectures as well as to teach implementation, training and debugging own neural networks dedicated to visual recognition. Students gain theoretical knowledge, information on the state of the current research in the domain and obtain practical skills in visual recognition. |
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