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Entourage: Visualizing Relationships between Biological Pathways using Contextual Subsets

Lex, Alexander and Partl, Christian and Kalkofen, Denis and Streit, Marc and Gratzl, Samuel and Wassermann, Anne and Schmalstieg, Dieter and Pfister, Hanspeter (2013) Entourage: Visualizing Relationships between Biological Pathways using Contextual Subsets. IEEE Transactions on Visualization and Computer Graphics, 19 (12). pp. 2536-2545. ISSN 1077-2626

Abstract

Biological pathway maps are highly relevant tools for many tasks in molecular biology. They reduce the complexity of the overall biological network by partitioning it into smaller manageable parts. While this reduction of complexity is their biggest strength, it is, at the same time, their biggest weakness. By removing what is deemed not important for the primary function of the pathway, biologists lose the ability to follow and understand cross-talks between pathways. Considering these cross-talks is, however, critical in many analysis scenarios, such as, judging effects of drugs. In this paper we introduce Entourage, a novel visualization technique that provides the contextual information lost because of the artificial partitioning of the biological network, but at the same time limits the presented information to what is relevant to the user’s task. We use one pathway map as the focus of an analysis and allow a larger set of contextual pathways. For these context pathways we only show the contextual subsets, i.e., the parts of the graph that are relevant to a current selection. Entourage suggests related pathways based on similarities and highlights parts of a pathway that are interesting in terms of mapped experimental data. We visualize interdependencies between pathways using stubs of visual links, which we found effective yet not obtrusive. By combining this approach with visualization of experimental data, we can provide domain experts with a highly valuable tool. We demonstrate the utility of Entourage with case studies conducted with a biochemist who researches the effects of drugs on pathways. We show that the technique is ideally suited to investigate interdependencies between pathways and to analyze, understand, and predict the effect that drugs have on different cell types.

Item Type: Article
Date Deposited: 13 Oct 2015 13:13
Last Modified: 06 Jul 2016 23:45
URI: https://oak.novartis.com/id/eprint/10692

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