Vorlesung: Bioinformatics II


  • Dozent(en):
  • Beginn: 25.10.2016
  • Zeiten: Di. 9:30-11:00 B-IT Marschallsaal
  • Studiengang: B-IT Master Life Science Informatics
  • Aufwand: 2 SWS / 3 CP



This lecture starts on Tuesday, October 25.

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In recent years, methods from visual computing have gained significant and increasing importance within bioinformatics. This is due to two main factors:

  1. Given ever-increasing volumes of sequencing data, suitable analysis and understanding of multi-dimensional data replaces data generation as the bottleneck in gaining biological insight. Graphical representations can augment our ability to reason about such data, and integrating them with automated data analysis allows scientists to better understand and steer data processing.
  2. Biology makes increasing use of digital images, primarily from microscopy and Magnetic Resonance Imaging (MRI). Performing quantitative analysis of such data requires suitable methods for image processing, including image registration and segmentation.

The range of available tools is as diverse as the research questions and methods in biology. Our class focuses on important basic principles of human perception, techniques for visualization of multi-dimensional data and graphs, image segmentation, registration, and statistical analysis, and provides some specific example applications within biology.

In addition to the lecture, students will complete two hands-on practical projects, one on data visualization, the other one on image processing, over the course of the semester.




Übung 1: Installation
Übungsblatt  (PDF-Dokument, 118 KB)
Übung 2: Data-manipulation
Übungsblatt  (PDF-Dokument, 147 KB)
Übung 3: Scatter-Plot-Matrix
Übungsblatt  (PDF-Dokument, 108 KB)
Übung 4: Dimensionality-Reduction
Übungsblatt  (PDF-Dokument, 110 KB)
Übung 5: Graph-Visualization
Übungsblatt  (PDF-Dokument, 128 KB)
Übung 6: Image-Processing
Übungsblatt  (PDF-Dokument, 117 KB)
Übung 7: Gaussian-Mixture-Models
Übungsblatt  (PDF-Dokument, 174 KB)
Übung 8: MRFs
Übungsblatt  (PDF-Dokument, 110 KB)
Übung 9: StatisticalImgAnalysis-GLM
Übungsblatt  (PDF-Dokument, 190 KB)