Detail předmětu

Mentoring 1

FEKT-DKA-MN1Ak. rok: 2020/2021

Doporučená nebo povinná literatura

ALBERTS, Bruce. Molecular biology of the cell. Sixth edition. New York, NY: Garland Science, Taylor and Francis Group, 2015. ISBN 9780815344322. (EN)
LAKOWICZ, Joseph R. Principles of fluorescence spectroscopy. 3rd ed. New York: Springer, c2006. ISBN 978-0-387-31278-1. (EN)
ROBINSON, Andrew J a Lynn SNYDER-MACKLER. Clinical electrophysiology: electrotherapy and electrophysiologic testing. 3rd ed. Philadelphia: Wolters Kluwer Health/Lippincott Williams & Wilkins, c2008. ISBN 978-0-7817-4484-3. (EN)
AKAY, Metin. Wiley encyclopedia of biomedical engineering. Hoboken, N.J.: Wiley-Interscience, c2006. ISBN 978-0-471-24967-2. (EN)
JAN, Jiří. Digital signal filtering, analysis and restoration. London: Institution of Electrical Engineers, 2000. IEE telecommunications series, 44. ISBN 0852967608. (EN)
JAN, Jiří. Medical image processing, reconstruction, and restoration: concepts and methods. Boca Raton, FL: Taylor & Francis, 2006. ISBN 9780824758493. (EN)
COMPEAU, Phillip a Pavel PEVZNER. Bioinformatics algorithms: an active learning approach. La Jolla, CA: Active Learning Publishers, 2014. ISBN 978-0990374602. (EN)
StatSoft, Inc. (2013). Electronic Statistics Textbook. Tulsa, OK: StatSoft. WEB: http://www.statsoft.com/textbook/. (EN)

Jazyk výuky

angličtina

Osnovy výuky

The subject is focused on specific research topics individualized for PhD students with regard to the focus of their dissertations. The course is taught individually. A mentor (internal or external academic staff, or a professional expert) is assigned to each PhD student. The mentor is specialized in issues from a wider range of theoretical knowledge and professional skills related to a particular research topic. In the case of thematic affinity of dissertations of more doctoral students the course is taught in broader teams. The course covers topics from the fields of cell biology, electrophysiology, signal and image processing and analysis, machine learning, bioinformatics, computational biology and statistics.

Specific topics of the course:
- methods of preprocessing, analysis and visualization of high frequency ECG signals
- optimal methods of ECG signal quality estimation
- methods of biosignals aggregation for multisensor systems of human activity monitoring
- mental status detection methods based on brain biosignal analysis and video monitoring
- methods of medical image data analysis from spectral computed tomography,
- multimodal holographic microscope image analysis methods for cell studies
- deep learning methods for image data processing in biometrics
- bioinformatic methods of gene expression analysis
- methods of reconstruction of gene regulatory networks.

Controlled outputs of the course:
- a detailed study plan,
- prepared materials for teaching (functional sample, software, laboratory task),
- prepared a proposal for an internal grant project for a university specific research program,
- prepared and submitted application for the competition "Brno Ph.D. Talent",
- prepared and submitted abstract at a conference of international importance,
- detailed mentor evaluation.

Zařazení předmětu ve studijních plánech

  • Program DKA-BTB doktorský, 1. ročník, zimní semestr, 4 kredity, povinný

Typ (způsob) výuky

 

Seminář

26 hod., nepovinná

Vyučující / Lektor

eLearning