@inproceedings{partl_enroute:_2012,
	title = {{enRoute}: Dynamic Path Extraction from Biological Pathway Maps for In-Depth Experimental Data Analysis},
	shorttitle = {{enRoute}},
	doi = {10.1109/BioVis.2012.6378600},
	abstract = {Pathway maps are an important source of information when analyzing functional implications of experimental data on biological processes. However, associating large quantities of data with nodes on a pathway map and allowing in depth-analysis at the same time is a challenging task. While a wide variety of approaches for doing so exist, they either do not scale beyond a few experiments or fail to represent the pathway appropriately. To remedy this, we introduce {enRoute}, a new approach for interactively exploring experimental data along paths that are dynamically extracted from pathways. By showing an extracted path side-by-side with experimental data, {enRoute} can present large amounts of data for every pathway node. It can visualize hundreds of samples, dozens of experimental conditions, and even multiple datasets capturing different aspects of a node at the same time. Another important property of this approach is its conceptual compatibility with arbitrary forms of pathways. Most notably, {enRoute} works well with pathways that are manually created, as they are available in large, public pathway databases. We demonstrate {enRoute} with pathways from the well-established {KEGG} database and expression as well as copy number datasets from humans and mice with more than 1,000 experiments. We validate {enRoute} using case studies with domain experts, who used {enRoute} to explore data for glioblastoma multiforme in humans and a model of steatohepatitis in mice.},
	booktitle = {Proceedings of the {IEEE} Symposium on Biological Data Visualization ({BioVis} '12)},
	author = {Partl, Christian and Lex, Alexander and Streit, Marc and Kalkofen, Denis and Kashofer, Karl and Schmalstieg, Dieter},
	year = {2012},
	pages = {107--114}
}