For example, in the case of protein-protein interaction networks, such clusters comprise proteins forming protein complexes or acting in related signaling pathways. Within large networks of ~100,000 nodes, NaviClusterCS rapidly finds biologically meaningful clusters, which are sets of nodes that are densely connected to each other and/or share similar biological functions (see Implementation). Herein, we describe our development of NaviClusterCS, which enables researchers to navigate biological networks in a Google Maps-like manner in the Cytoscape environment. In addition to its built-in sophisticated features, users can easily extend Cytoscape by loading extra plug-ins. This method, which is similar to online mapping services such as Google Maps, can rapidly provide appropriately abstracted views at any magnification and enable researchers to effectively interpret networks.Ĭytoscape is an OS-independent, de facto standard platform for network visualization and has many users around the world. We previously developed an interactive, multi-scale navigation method for large biological networks. To overcome this problem, effective navigation approaches that can abstract data properly and present them insightfully at any magnification are required. Instead of being helpful to biologists, such network representations cannot be visually interpreted or further analyzed to extract meaningful biological facts. Network visualization is widely used to represent such data (e.g., protein-protein interactions and gene co-expressions) however, it does not work effectively with the big data due to the jumble of tangled edges ("hair-balls"). The exponentially increasing amount of functional genomics data is significantly inhibiting researchers from making sense of these data.
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