Biological Network Visualization
Approaches to investigate biological processes have been of strong interest in the past few years and are the focus of several research areas like systems biology. Biological networks as representations of such processes are crucial for an extensive understanding of living beings. Due to their size and complexity, their growth and continuous change, as well as their compilation from databases on demand, researchers very often request novel network visualization, interaction and exploration techniques.
There are still many challenges related to the visualization of large and complex biological networks. Among others, we addressed the interactive visual analysis of the results of centrality computations in context of biological networks. An important analytical aspect here is to examine nodes according to specfic centrality values and to compare them. We also researched mappings between the gene ontology and cluster trees and developed a novel visualization method for this task that offers a holistic visualization approach bringing both data sets together. Moreover, a general solution for visualizing interconnected biological pathways was invented. Our visualization approach supports the analyst in obtaining an overview to related pathways. By using glyphs, brushing, and topological information of the related pathways, our approach intuitively guides the exploration and navigation process. Finally, we proposed a new web-based visualization environment, called OnGraX, which supports distributed, synchronous and asynchronous collaboration when analyzing biological networks together with several experts.
The Biological Network Visualization project was performed from 2009 until 2018 in collaboration with several research partners for this topic, especially the Network Analysis Group at the IPK Gatersleben, the Image and Signal Processing Group at Leipzig University, and the Center for Bioinformatics at the Saarland University, all Germany.
Relevant Publications:
Relevant Tools:
Interesting URLs: