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Hannah Tipney (University of Colorado at Denver Health Sciences Center, Aurora, United States of America)
As scientists embrace the systems biology approach to biomedical research, many network-based tools have become available to researchers seeking to understand complex interactions on a genomic scale. Though large-scale network analysis holds the promise of providing considerable insight and understanding, analyzing these networks often pose significant barriers to their use. We describe our attempt to address these barriers in relation to the Hanalyzer (http://hanalyzer.sourceforge.net/), a 3R (reading, reasoning and reporting) system that combines knowledge and data in a network-based manner. Removing barriers associated with network analysis was achieved through a number of methods, including placing a strong emphasis on tracking provenance information for edges in a network lessens the ‘black box’ effect commonly cited by biologists when original data is difficult to observe. Visualization aids in the form of Cytoscape plugins enable the user to readily explore edge provenance. Tools to monitor changes in networks over time and to support users in some of the most taxing exploratory tasks, e.g. interpreting a ‘yar