Nucleic Acids Research, 2012, Vol. 40, Web Server issue W125 Figure 1. Snapshots of the SurvNet web server. (A) Input page, through which input ﬁles and a search parameter can be speciﬁed. (B) Output page, on which the top subnetwork biomarkers identiﬁed are displayed. (C) Visualization page, on which subnetworks can be visualized in a user-friendly way. from high to low score. Each score is associated with the output items that follow. The network score, which quantiﬁes how signiﬁcantly the nodes in a subnetwork correlate with the observed patient survival data, is calculated based on the univariable Cox proportional hazards model and the network properties. A higher network score indicates a more signiﬁcant correlation between the network and the patient survival time. The ‘Cox P-value’ is the P-value derived from the multivariable Cox proportional hazards regression model. The ‘gene_ID’ indicates the seeded node for each subnetwork; the number of nodes indicates how many genes (or proteins) are in the subnetwork; and the number of edges indicates how many interactions are in the subnetwork. Users can further narrow down the results by two output parameters: network P-value cut-off and minimal number nodes. The network P-value cut-off determines how signiﬁcant the returned subnetworks are compared to the random background; and the default signiﬁcance level is 0.05. Minimal number nodes determine the minimal number of nodes in a subnetwork; the default value is 2. After clicking the ‘Graph’ button in the ﬁnal output page, users can visualize an identiﬁed subnetwork in a user-friendly Java applet that allows them to pan/zoom, search and retrieve useful information (from GeneCard) for a node of interest. A detailed description about the Java applet is available under the visualization page. CONCLUSION We have developed SurvNet, a web server that can efﬁ- ciently identify network-based biomarkers that most cor- relate with patient survival data. To the best of our knowledge, SurvNet is the only available bioinformatics tool for this function. SurvNet uses the network-based biomarker searching algorithms that were established in previous studies, and provides a user-friendly interface for exploring the identiﬁed biomarkers. We expect SurvNet to be a valuable resource for generating meaningful hypotheses for disease diagnosis and treatment. ACKNOWLEDGEMENTS We thank Bradley Broom, Jun Zhang, Allen Chang, Chris Wakeﬁeld, Glen Brosch and Sylvain Laroche for their help in building and maintaining the web server. FUNDING The University of Texas MD Anderson Cancer Center, the National Institutes of Health [U24 CA143883 and P30 CA016672]; G.S. Hogan Gastrointestinal Research fund and the Lorraine Dell Program in Bioinformatics for Personalization of Cancer Medicine (to H.L.). Funding for open access charge: The University of Texas MD Anderson Cancer Center.