Course detail

Mentoring 2

FEKT-DPA-MN2Acad. year: 2022/2023

Not applicable.

Learning outcomes of the course unit

Not applicable.

Prerequisites

Not applicable.

Co-requisites

Not applicable.

Recommended optional programme components

Not applicable.

Recommended or required reading

PARYS, Jan B, Martin D BOOTMAN, David I YULE a Geert BULTYNCK. Calcium techniques: a laboratory manual. Cold Spring Harbor, New York: Cold Spring Harbor Laboratory Press, 2014. ISBN 978-1936113583. (EN)
PAWLEY, James B. Handbook of biological confocal microscopy. 2nd ed. New York: Plenum Press, c1995. ISBN 978-0306448263. (EN)
ZIPES, Douglas P a José JALIFE. Cardiac electrophysiology: from cell to bedside. Sixth edition. Philadelphia, PA: Elsevier/Saunders, 2014. ISBN 978-1455728565. (EN)
SONKA, Milan, Vaclav HLAVAC a Roger BOYLE. Image processing, analysis, and machine vision. 3rd ed. Toronto: Thompson Learning, c2008. ISBN 978-0495082521. (EN)
SHMULEVICH, Ilya a Edward R DOUGHERTY. Genomic signal processing. Princeton, N.J.: Princeton University Press, c2007. ISBN 0691117624. (EN)
HIGGS, Paul G a Teresa K ATTWOOD. Bioinformatics and molecular evolution. Malden, Mass.: Blackwell Publishing, 2005. ISBN 1-4051-0683-2. (EN)
BOZDOGAN, H. Statistical data mining and knowledge discovery. Boca Raton, FL: Chapman & Hall/CRC, c2004. ISBN 1-58488-344-8. (EN)

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

Not applicable.

Language of instruction

English

Work placements

Not applicable.

Course curriculum

The subject is focused on specific research topics individualized for individual doctoral students with regard to the focus of their dissertations. This subject significantly extends the topics and solutions of a particular doctoral student within the subject Mentoring 1. The course is taught individually. A mentor (internal or external academic staff, or a professional expert) is assigned to each doctoral 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 pre-processing biosignals from wearable devices,
- methods of heart rate variability reconstruction from image data
- methods of predicting urgent states from wearable devices signals,
- methods of determining sports activity from continuous biosignal records,
- methods of determining the disease extent and progression from CT image data
- fluorescence methods for monitoring the movement of stem cells in tissue
- methods of controlled tissue growth by linking living cells in a matrix,
- methods of whole genome analysis of gene expression in bacteria,
- methods of computer-aided drug design using natural molecules.

Controlled outputs of the course:
- a detailed study plan,
- prepared materials for teaching (functional sample, software, laboratory task),
- research project report
- prepared and submitted review article on a topic related to a dissertation
- detailed mentor evaluation.

Aims

Not applicable.

Classification of course in study plans

  • Programme DPA-BTB Doctoral, 1. year of study, summer semester, 4 credits, compulsory

Type of course unit

 

Seminar

26 hours, optionally

Teacher / Lecturer