Big Data in Biomedicine (DN_181)
About Study Course
Objective
To acquaint doctoral students with the sources and types of big data in the modern biomedicine, as well as to give the first insight into the processing and interpretation of this data. The main focus of the course will be on the so-called OMICS data, namely GENOME, EPIGENOME, TRANSCRIPTOME, PROTEOME, METABOLOME, MICROBIOME data, as well as the integration of these data with clinical, environmental and life-style data or CLINOME/ENVIROME.
Prerequisites
Study courses in medicine or biology, preferably with additional prior knowledge in statistics and computer programming.
Learning outcomes
The doctoral student has gained understanding of the sources and types of large data in modern biomedicine (GENOME, EPIGENOME, TRANSCRIPTOME, PROTEOME, METABOLOME, MICROBIOME and CLINOME / ENVIROME).
The doctoral student has basic skills in handling big data. The doctoral student is able to critically analyse and explain the results obtained from big data.
The doctoral student is well familiar with the main big data analyses tools, methods and workflows and their basic principles, used by bioinformaticians.
Study course planning
| Study programme | Study semester | Program level | Study course category | Lecturers | Schedule |
|---|---|---|---|---|---|
| Health Care (subprogramme Pharmacy), DVAfeng | 3 | Doctoral | Limited choice | ||
| Health Care (subprogramme Medicine), DVAmeng | 3 | Doctoral | Limited choice |
