Published online 8 May 2012 Nucleic Acids Research, 2012, Vol. 40, Web Server issue W123–W126 doi:10.1093/nar/gks386 SurvNet: a web server for identifying network-based biomarkers that most correlate with patient survival data Jun Li1,2, Paul Roebuck 3 , Stefan Gru¨ newald1 and Han Liang2,* 1Chinese Academy of Sciences Key Laboratory of Computational Biology, Chinese Academy of Sciences and Max Planck Society (CAS-MPG) Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, P.R. China, 2Department of Bioinformatics and Computational Biology and 3Division of Quantitative Sciences, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA Received March 2, 2012; Revised April 11, 2012; Accepted April 14, 2012 ABSTRACT An important task in biomedical research is identify- ing biomarkers that correlate with patient clinical data, and these biomarkers then provide a critical foundation for the diagnosis and treatment of disease. Conventionally, such an analysis is based on individual genes, but the results are often noisy and difficult to interpret. Using a biological network as the searching platform, network-based bio- markers are expected to be more robust and provide deep insights into the molecular mechan- isms of disease. We have developed a novel bioinformatics web server for identifying network- based biomarkers that most correlate with patient survival data, SurvNet. The web server takes three input files: one biological network file, representing a gene regulatory or protein interaction network; one molecular profiling file, containing any type of gene- or protein-centred high-throughput biological data (e.g. microarray expression data or DNA methylation data); and one patient survival data file (e.g. patients’ progression-free survival data). Given user-defined parameters, SurvNet will automatically search for subnetworks that most correlate with the observed patient survival data. As the output, SurvNet will generate a list of network biomarkers and display them through a user-friendly interface. SurvNet can be accessed at http://bioinformatics .mdanderson.org/main/SurvNet. INTRODUCTION With the advance of genome characterization technology, high-throughput genomic and proteomic data of patients have accumulated rapidly, allowing the systematic identi- ﬁcation of biomarkers (1–4). Biomarkers that correlate with patient survival data are of particular interest because they provide a critical foundation for the diagno- sis and treatment of disease (5,6). Conventionally, such an analysis is based on individual genes. However, the results thereby obtained are often noisy and difﬁcult to interpret the underlying mechanisms of disease. Biological networks (e.g. gene regulatory networks or protein interaction networks) represent a reasonable way to summarize the functional behaviours of components within a biological system (7–9). Therefore, using a biological network as the searching platform, network-based biomarkers (i.e. a group of functionally related genes or proteins) are expected to be more robust and provide valuable insights into the molecular mechanisms of disease. Previous studies (10,11) on this topic have focused on other clinical data (such as metastasis status), and the utility of patient survival data has not been explored. In this study, we introduce SurvNet, a novel bioinfor- matics web server for identifying network-based biomarkers that most correlate with patient survival data. The web server takes three input ﬁles: one biological network ﬁle, one molecular proﬁling ﬁle and one patient survival data ﬁle. In order to identify network-based biomarkers, SurvNet uses established algorithms (10–12) for searching and evaluating the biomarkers. As the output, SurvNet generates a user-friendly display of network-based biomarkers. We expect SurvNet to be a valuable bioinformatics tool for the biomedical community. MATERIALS AND METHODS The computational approach used by SurvNet to iden- tify network-based biomarkers consists of three compo- nent processes: (i) a scoring function (combining the *To whom correspondence should be addressed. Tel: +1 713 745 9815; Fax: +1 713 563 4242; Email: hliang1@mdanderson.org The Author(s) 2012. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/ by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.