(in Polish) Analiza danych biomedycznych

Master's seminars

The seminar topics include computational biology and machine learning in application to biomedical data analysis. We are interested in the problems of human diseases such as cancer or infectious diseases. From the area of computational biology we focus on analysis of modern molecular profiling data, analysis of single cell sequencing data, medical imaging, or protein structure. From methods, we focus on probabilistic graph models, statistical data analysis, machine learning, including deep learning, and generative models.

Modern medical challenges often involve diseases with complex genetic and molecular backgrounds. Modern molecular profiling methods yield vast resources of tabular or imaging data. Analysis of these data can help understand how diseases such as cancer or infectious diseases arise, how they work, and how to treat them.

Biomedical data analysis is a very capacious field of research that uses a variety of mathematics and computer science methods: artificial intelligence, machine learning, probabilistic methods, statistics.

It is currently a very intensively developing field of interest for both private companies and all leading universities.

The topics of the seminar focus on molecular data analysis methods. Many papers deal with current research projects in which the research groups leading the seminar are involved. Our recent interests include the following topics:

- Antimicrobial resistance. We are developing specialized deep generative models for the generation of synthetic antimicrobial peptides that can kill antibiotic-resistant bacteria (Methods used: deep learning, generative models).

- Modeling the tumor microenvironment. What is the spatial organization of the tumor and its neighborhood? How do they interact with each other? These interesting questions can be addressed using spatial transcriptomics, digital tumor imaging or mass spectrometry data. (Methods: probabilistic graphical models, machine learning models).

- Drawing cancer family trees. Which cancer mutations appear first? How do metastases arise? How does drug resistance arise in cancer? Is cancer evolution neutral or driven by selection? These and many other questions about the family history of cancer cells are very exciting for us! (Methods: probabilistic graphical models, mathematical models).

- Fighting cancer with its own weapons. We analyze the phenomenon of synthetic lethality between cancer genes. We can use synthetic lethality to treat cancer. When one gene is mutated, we can target its synthetic lethality to kill cancer cells. (Methods: statistical tests, survival analysis methods).

- Deep Pathologist. Can deep learning algorithms improve the work of pathologists? Artificial intelligence methods, such as convolutional neural networks, can be trained on histological images of tumors to recognize multiple tissue types. (Methods: deep learning models).

- Modeling drug efficacy. We attempt to understand and predict how drugs act on cancer cell lines. (Methods: statistical models, optimization algorithms).

- Mutagenic processes in cancer. The mutation landscape of a cancer genome is a result of complex interactions between DNA damage, DNA repair, and other biological processes. Such processes can be studied through the lenses of characteristic mutation patterns imprinted by individual mutagens. We analyze these patterns, link them to specific causes, uncover and model interactions between them. (Methods: statistical data analysis, probabilistic methods, machine learning.)