Lecture: Visual Computing in the Life Sciences

Course

  • Lecturer(s):
  • Start: 20.04.2020
  • Dates: Mon 11:00-12:30 and Thu 10:30-12:00 (online)
  • Curriculum: B-IT Master Life Science Informatics
  • Effort: 4 SWS / 6 CP
  • Exams: TBA

Exercises

Description

Important: This lecture will start on April 20 in an online only format. Until further notice, all lectures and exercises will be held via video conferencing. Access information will be distributed via our mailing list. We will continue using the list from last semester's Computer Science for Life Scientists. If you need to subscribe or unsubscribe, please externdo it here.

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

  1. Given ever-increasing volumes of scientific data, suitable analysis and understanding of multi-dimensional data replaces data generation as the bottleneck in gaining scientific insight. Visual representations can augment our ability to reason about such data. 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. Performing quantitative analysis based on such data requires suitable methods for image processing and analysis.

Within this scope, this year's lecture will focus on the following main topics:

  • Basic principles of human visual perception
  • Visualization of multidimensional data
  • Dimensionality reduction and manifold learning
  • Graph visualization
  • Design and analysis of visualizations for the life sciences
  • Biomedical imaging techniques
  • Basic image processing
  • Image analysis with deep learning
  • Modeling and analysis of neuroimaging data

Slides

Assignment Sheets

Exercise 1: Data-manipulation
Assignment sheet  (PDF document, 178 KB)
Exercise 2: Scatter-Plots-PCP
Assignment sheet  (PDF document, 146 KB)
Exercise 3: Dimensionality-Reduction
Assignment sheet  (PDF document, 164 KB)
Exercise 4: GraphVis
Assignment sheet  (PDF document, 252 KB)