| Study program / study programs: Advanced data analytics |
| Course: Data analysis in biological sciences |
| Teacher(s): Prof. Dr Marko Đorđević, Prof. Dr Biljana Miličić, Doc. Dr Andrej Korenić, Ass. Dr Jovana Kuzmanović Pfićer, Dr Tijana Išić Denčić |
| Course status: Elective |
| ECTS points: 7 |
| Prerequisites: none |
| Course objective: Analyzing complex biological data |
| Learning outcomes: Students will acquire new knowledge and skills in the field of data analysis in biology. Modern computational methods used in molecular and cell biology will be presented. Students will also be able to analyse complex data obtained by processing microscopic images in histology. In addition to the image processing, this subject will also focus on aspects of signal processing in neurosciences such as resting membrane potential, action potential, local potentials, etc. Students will also be familiarized with the foundations of biostatistics, and with applying modern statistical tests in biological sciences. |
| Course structure and content: Introduction to data analysis in biology Nucleic acids and protein databases Basic analysis of nucleic acids and protein sequences Analysis of data obtained by light and electron microscopy Analysis of the resting membrane potential and action potential Evaluation of local membrane potentials: application in neurosciences Statistical analytical tests in biological research Correlation and regression analysis in biology |
| Literature/Readings: Rafael A. Irizarry. Data Analysis for the Life Sciences with R. 2017. Chapman and Hall/CRC. New York. S.J. Welham, S.A. Gezan, S.J. Clark, A. Mead. Statistical Methods in Biology: Design and Analysis of Experiments and Regression. 2014. Chapman and Hall/CRC. New York. Daniel Durstewitz. Advanced Data Analysis in Neuroscience. Springer International Publishing. 2017. London, UK Zvelebil, Marketa J., and Jeremy O. Baum. Understanding bioinformatics. Garland Science, 2007. Shortliffe, E.H., Cimino, J.J. Biomedical Informatics: computer applications in healthcare and biomedicine. 4th Edition, Kindle Edition. Springer-Verlag London 2014 |
| The number of class hours per week: Lectures: 5 Labs: 0 Workshops: 1 Research study: 2 Other classes: 0 |
| Teaching methods: Individual and group work; lectures and labs |
| Evaluation/Grading (maximum 100 points): Pre-exam requirements (Project, conceptual solution): 30 Project (implementation): 70 |
