We present the usage of ZoomOut Webserver on gene interaction networks regarding significantly differentially expressed genes identified between 43 ERa-positive breast tumors including 14 tumors with PIK3CA mutations and 29 tumors without PIK3CA mutations obtained from Gene Expression Omnibus (GEO) (http://www.ncbi.nlm.nih.gov/geo/) database under series accession numbers GSE22035 . These gene interaction networks have been produced using 11 widespread network inference methods. More specifically, six methods based on mutual information (Aracne.a , Aracne.m , MRNET , MRNETB , CLR  and C3net ), four correlation based (Genenet , WGCNA , Lasso , Adaptive Lasso ) and one tree based (Genie3 ), were applied in the expression values of the top 100 differentially expressed genes based on their p-values. Finally, for the network reconstruction based on biological information we used the Cytoscape platform and more specifically the GeneMania plugin .
Upload Data to Workspace
Users are able to upload their networks by pressing �Choose file� button, selecting multiple files (Ctrl+A for all files), and then pressing �Upload to Workspace� button. Users are also able to download and visualize their uploaded data.
Download or Visualize Data
Users are able to navigate to the graph either by holding left mouse button and scrolling mouse or by using the navigation panel. By selecting a node in the graph, 1st neighborhood connections are only visible. By clicking again outside of the graph, the whole network is revealed again. Initial network is in random layout, however users are able to select between random, circular or Fruchterman-Reingold layout (as shown here). Nodes of the network can also be found and selected using �select node�.
2. Feature Networks Caclucations
Users are able to generate eleven global network characteristics � features by pressing �Generate Features�.
Global Network Features
3. Clustering Networks
In the present study, we have selected Affinity Propagation Clustering with Euclidean distance. As features we have selected Jaccard index, degree of distribution (mean), clustering distribution (mean) and closeness centrality (mean).
After applying clustering, the following clustering image (dendrogram) is generated. Affinity Propagation Clsutering
By clicking the Visualize button, all networks with their calculated distances can be displayed. The larger the connection of two networks, the more bold the interconnected edge will be displayed
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