PCSF: An R-package for network-based interpretation of high-throughput data.
PCSF: An R-package for network-based interpretation of high-throughput data.
复制标题
DOI：
10.1371/journal.pcbi.1005694
复制
发表时间：
2017-07
影响因子：
4.3
通讯作者：
Kwee I
中科院分区：
文献类型：
--
作者：
Akhmedov M;Kedaigle A;Chong RE;Montemanni R;Bertoni F;Fraenkel E;Kwee I
随着最近技术的发展，大量的高通量数据被用来理解复杂疾病的机制。当前的生物信息学挑战是解释数据和潜在的生物学，其中使用生物网络分析异构高通量数据的有效算法变得越来越有价值。在本文中，我们提出了一个基于获奖斯坦纳森林图优化方法的软件包。PCSF软件包通过将数据映射到生物网络（如蛋白质-蛋白质相互作用、基因-基因相互作用或任何其他基于相关或共表达的网络）上，对高通量数据进行快速和用户友好的网络分析。使用交互网络作为模板，它确定与数据相关的高置信度子网，这可能导致功能单元的预测。它还通过功能富集分析交互式地可视化生成的子网。
With the recent technological developments a vast amount of high-throughput data has been profiled to understand the mechanism of complex diseases. The current bioinformatics challenge is to interpret the data and underlying biology, where efficient algorithms for analyzing heterogeneous high-throughput data using biological networks are becoming increasingly valuable. In this paper, we propose a software package based on the Prize-collecting Steiner Forest graph optimization approach. The PCSF package performs fast and user-friendly network analysis of high-throughput data by mapping the data onto a biological networks such as protein-protein interaction, gene-gene interaction or any other correlation or coexpression based networks. Using the interaction networks as a template, it determines high-confidence subnetworks relevant to the data, which potentially leads to predictions of functional units. It also interactively visualizes the resulting subnetwork with functional enrichment analysis.
登录
查看更多内容
HMDB：人类代谢组数据库。
影响因子：
14.9
作者：
Wishart, David S;Tzur, Dan;Knox, Craig;Eisner, Roman;Guo, An Chi;Young, Nelson;Cheng, Dean;Jewell, Kevin;Arndt, David;Sawhney, Summit;Fung, Chris;Nikolai, Lisa;Lewis, Mike;Coutouly, Marie-Aude;Forsythe, Ian;Tang, Peter;Shrivastava, Savita;Jeroncic, Kevin;Stothard, Paul;Amegbey, Godwin;Block, David;Hau, David D;Wagner, James;Miniaci, Jessica;Clements, Melisa;Gebremedhin, Mulu;Guo, Natalie;Zhang, Ying;Duggan, Gavin E;Macinnis, Glen D;Weljie, Alim M;Dowlatabadi, Reza;Bamforth, Fiona;Clive, Derrick;Greiner, Russ;Li, Liang;Marrie, Tom;Sykes, Brian D;Vogel, Hans J;Querengesser, Lori
通讯作者：
Querengesser, Lori
影响因子：
1.7
作者：
Tuncbag, Nurcan;Braunstein, Alfredo;Fraenkel, Ernest
通讯作者：
Fraenkel, Ernest
DOI：
10.1073/pnas.1004751108
发表时间：
2011-01-11
影响因子：
11.1
作者：
Bailly-Bechet, M.;Borgs, C.;Zecchina, R.
通讯作者：
Zecchina, R.
影响因子：
4.6
作者：
Akhmedov, Murodzhon;Kwee, Ivo;Montemanni, Roberto
通讯作者：
Montemanni, Roberto
影响因子：
64.8
作者：
Mertins, Philipp;Mani, D. R.;Ruggles, Kelly V.;Gillette, Michael A.;Clauser, Karl R.;Wang, Pei;Wang, Xianlong;Qiao, Jana W.;Cao, Song;Petralia, Francesca;Kawaler, Emily;Mundt, Filip;Krug, Karsten;Tu, Zhidong;Lei, Jonathan T.;Gatza, Michael L.;Wilkerson, Matthew;Perou, Charles M.;Yellapantula, Venkata;Huang, Kuan-lin;Lin, Chenwei;McLellan, Michael D.;Yan, Ping;Davies, Sherri R.;Townsend, R. Reid;Skates, Steven J.;Wang, Jing;Zhang, Bing;Kinsinger, Christopher R.;Mesri, Mehdi;Rodriguez, Henry;Ding, Li;Paulovich, Amanda G.;Fenyo, David;Ellis, Matthew J.;Carr, Steven A.
通讯作者：
Carr, Steven A.