About Course
This course teaches data-specific computational analyses and integrative analysis approaches that combine knowledge from different OMICs-based datasets, covering both theoretical and practical aspects.
Course abbreviation: INT-BIO
Instructor: Dr. Mohamed Hamed
Head of the Integrative OMICs Analysis Group in Rostock University Medical Center, Rostock University, Germany
Workload: 12 lectures, 3 hours each. Total workload: 42 hours: 36 hours of lectures and tutorials and 6 hours of self studies.
Entrance requirements: Basic knowledge of biology and computer science.
Used media: PowerPoint presentation
Objectives
- Perform data-specific computational analyses
- Apply integrative analysis approaches using multiple OMICs datasets
- Identify biomarkers for early diagnosis and prognosis of complex diseases
- Develop treatment-related insights using integrative bioinformatics
Competences to be Developed
- R scripting language and Bioconductor packages
- Data-specific computational analysis and pipelines
- Developing and applying integrative bioinformatics methods
- Basics of machine learning methods for integrating heterogeneous omics data
- Research project development, result interpretation, manuscript writing, and scientific discussion
Assessment
- Finalize a research project applying learned methods
- Compile project outcomes into a high-quality research article ready for peer-review submission
- Present and scientifically review all projects in the last lecture
Course Content
Lecture 1: R Language Mini-course 1
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R Language Mini-course 1
00:00
Lecture 2: R Revision and Advanced R Packages
Lecture 3: Epigenetics
Lecture 4: Genetics
Lecture 5: Lipidomics
Lecture 6: Introduction to Integrative Bioinformatics
Lecture 7: Network-based Integrative Methods I
Lecture 8: Network-based Integrative Methods II
Lecture 9: Integrative Analysis Based on Machine Learning
Lecture 10: Multi-Omics Factorial Analysis (MOFA)
Lecture 11: DIABLO, SNF, Integrome and Dr.Dimont
Lecture 12: Projects Discussion and Closure
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