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Published on Dec 7, 2009
The goal of our work is to support experts in the process of hypotheses generation concerning the roles of genes in diseases. For a deeper understanding of the complex interdependencies between genes, it is important to bring gene expressions (measurements) into context with pathways. Pathways, which are models of biological processes, are available in online databases. In these databases, large networks are decomposed into small sub-graphs for better manageability. This simplification results in a loss of context, as pathways are interconnected and genes can occur in multiple instances scattered over the network. Our main goal is therefore to present all relevant information, i.e., gene expressions, the relations between expression and pathways and between multiple pathways in a simple, yet effective way. In our visualization system, we employ two different multiple-view approaches. Traditional multiple views are used for large datasets or highly interactive visualizations, while a 2.5D technique is employed to support a seamless navigation of multiple pathways which simultaneously links to the expression of the contained genes. The latter approach facilitates the understanding of the interconnection of pathways, and enables a non-distracting relation to gene expression data. Hypotheses derived from expression data can be quickly evaluated by exploring the complete biological context of an identified gene. We evaluated our system with a group of users from the life science community. Evaluation results show that the system can improve the process of understanding the complex network of pathways and the individual effects of gene expression regulation considerably.