97. Selection of signature genes for functional analysis and classification: the case study of astrocytoma
Anna Drozdz – Personal Health Data Science Team, Sano Centre for Computational Medicine, Krakow, PL
The development of high-throughput technologies has provided a strong foundation for the progress of modern systems biology. With the vast amount of biological data available, it is crucial to select those features that preserve the biological relevance of the data as well as those that can be used for therapeutic target discovery and classification purposes. To address this problem, many feature selection algorithms have been developed in recent years.
During this seminar, I will introduce the most important topics related to the DW MRI, including the diffusion-weighted data acquisition and diffusion tensor imaging. In addition, I will discuss multicompartment models and the accuracy of their use with diffusion-relaxometry data and various optimization algorithms, as well as tractography algorithms and their application in the analysis of brain maturation and aging.
About the author
Anna graduated from the Jagiellonian University in Kraków, Poland with a master’s degree in Laboratory Medicine and the AGH University of Science, Poland with an Engineering Degree in Materials Engineering. Anna completed her PhD studies at the Faculty of Physics, Astronomy and Computer Science of Jagiellonian University. In 2021 she defended her thesis titled: “Characterization and synthesis of drug carriers based on artificial exosomes dedicated to the treatment of microangiopathic diabetic complications”. Her doctoral thesis was based on the research project she led, which was awarded by the National Science Centre Poland.
Currently, she is a postdoctoral researcher at Sano – Centre for Computational Personalised Medicine – International Research Foundation in Kraków in the Personal Health Data Science Group. In her research she combines the knowledge which she gained during her studies in different areas. Anna is particularly interested in personalised medicine, disease prevention, biomarker discovery and diabetes.