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
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What Will You Learn?

  • 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
  • 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

Course Content

Lecture 1: R Language Mini-course 1

  • 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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