Lecture: Visual Computing in the Life Sciences

Course

  • Lecturer(s):
  • Start: 09.04.2018
  • Dates: Mon and Thu 10:30-12:00 INF/B-IT U.105
  • Curriculum: B-IT Master Life Science Informatics
  • Effort: 4 SWS / 6 CP
  • Exams: First: Thu Jul 19, 10-12 Second: Thu Aug 23, 10-12

Exercises

Description

In recent years, methods from visual computing have gained significant and increasing importance within bioinformatics and the life sciences. 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. The life sciences make 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 the life sciences. 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 bioinformatics.

Mailing list: We will continue using the list from last semester's Introduction to Computer Science. If you need to subscribe or unsubscribe, externdo it here.

Slides

Assignment Sheets

Exercise 1: Data-manipulation
Assignment sheet  (PDF document, 168 KB)
Exercise 2: Scatter-Plots-Dimensionality-Reduction
Assignment sheet  (PDF document, 129 KB)
Exercise 3: tSNE-Graph-Visualization
Assignment sheet  (PDF document, 187 KB)
Exercise 4: SpectralClustering-GMM
Assignment sheet  (PDF document, 211 KB)
Exercise 5: ImageFiltering-MRFs
Assignment sheet  (PDF document, 317 KB)
Exercise 6: DeepLearning
Assignment sheet  (PDF document, 127 KB)