A bioinformatics web app for identifying network-based biomarkers that most correlate with patient survival data. SurvNet is a valuable bioinformatic tool for identifying network-based biomarkers that most correlate with patient survival data. SurvNet takes three input files: one biological network file as the searching platform (one human protein interaction network is provided as default), one molecular profiling file (e.g., array-based gene expression or DNA methylation data or mutation data), and one patient survival data file. Given user-defined parameters, SurvNet will automatically identify sub-networks that most correlate with patient survival data and display the results in a visually appealing manner.
Chinese 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, China