SurvNet: a web server for identifying network-based biomarkers that most correlate with patient survival data.
SurvNet: a web server for identifying network-based biomarkers that most correlate with patient survival data.
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SurvNet：一个网络服务器，用于识别与患者生存数据最相关的基于网络的生物标志物
DOI：
10.1093/nar/gks386
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发表时间：
2012-07
影响因子：
14.9
通讯作者：
Liang H
中科院分区：
文献类型：
--
作者：
Li J;Roebuck P;Grünewald S;Liang H
生物医学研究中的一项重要任务是识别与患者临床数据相关的生物标志物，然后这些生物标志物为疾病的诊断和治疗提供关键基础。传统上，这种分析是基于单个基因，但结果往往是嘈杂的，难以解释。使用生物网络作为搜索平台，基于网络的生物标志物有望更加强大，并为疾病的分子机制提供深入的见解。我们开发了一种新型生物信息学网络服务器SurvNet，用于识别与患者生存数据最相关的基于网络的生物标志物。网络服务器有三个输入文件：一个生物网络文件，代表基因调控或蛋白质相互作用网络;一个分子分析文件，包含任何类型的基因或蛋白质为中心的高通量生物数据（例如微阵列表达数据或DNA甲基化数据）;和一个患者生存数据文件（例如患者的无进展生存数据）。给定用户定义的参数，SurvNet将自动搜索与观察到的患者生存数据最相关的子网络。作为输出，SurvNet将生成网络生物标志物列表，并通过用户友好的界面显示它们。SurvNet可在http://bioinformatics.mdanderson.org/main/SurvNet上访问。
An important task in biomedical research is identifying 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 biomarkers are expected to be more robust and provide deep insights into the molecular mechanisms 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.
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影响因子：
14.9
作者：
Keshava Prasad TS;Goel R;Kandasamy K;Keerthikumar S;Kumar S;Mathivanan S;Telikicherla D;Raju R;Shafreen B;Venugopal A;Balakrishnan L;Marimuthu A;Banerjee S;Somanathan DS;Sebastian A;Rani S;Ray S;Harrys Kishore CJ;Kanth S;Ahmed M;Kashyap MK;Mohmood R;Ramachandra YL;Krishna V;Rahiman BA;Mohan S;Ranganathan P;Ramabadran S;Chaerkady R;Pandey A
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Schreiber, Stuart L.;Shamji, Alykhan F.;Clemons, Paul A.;Hon, Cindy;Koehler, Angela N.;Munoz, Benito;Palmer, Michelle;Stern, Andrew M.;Wagner, Bridget K.;Powers, Scott;Lowe, Scott W.;Guo, Xuecui;Krasnitz, Alex;Sawey, Eric T.;Sordella, Raffaella;Stein, Lincoln;Trotman, Lloyd C.;Califano, Andrea;Dalla-Favera, Riccardo;Ferrando, Adolfo;Iavarone, Antonio;Pasqualucci, Laura;Silva, Jose;Stockwell, Brent R.;Hahn, William C.;Chin, Lynda;DePinho, Ronald A.;Boehm, Jesse S.;Gopal, Shuba;Huang, Alan;Root, David E.;Weir, Barbara A.;Gerhard, Daniela S.;Zenklusen, Jean Claude;Roth, Michael G.;White, Michael A.;Minna, John D.;MacMillan, John B.;Posner, Bruce A.
通讯作者：
Posner, Bruce A.
影响因子：
9.9
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影响因子：
5.7
作者：
Tibes, Raoul;Qiu, YiHua;Kornblau, Steven M.
通讯作者：
Kornblau, Steven M.