MINE: Module Identification in Networks.
MINE: Module Identification in Networks.
复制标题
DOI：
10.1186/1471-2105-12-192
复制
发表时间：
2011-05-23
影响因子：
3
通讯作者：
Gunsalus KC
中科院分区：
文献类型：
--
作者：
Rhrissorrakrai K;Gunsalus KC
网络关联的图形模型对于可视化和集成多种类型的关联数据非常有用。识别模块，或功能相关的基因产物组，是分析生物网络的一个重要挑战。然而，现有的识别模块的工具在应用于实验导出的交互数据的密集网络时是不够的。为了解决这个问题，我们开发了一种聚集聚类方法，能够在高度互联的分子相互作用网络中识别高度模块化的基因产物集。当应用于秀丽隐杆线虫蛋白-蛋白相互作用网络时，MINE在识别非排他性、高模块化集群方面优于MCODE、CFinder、NEMO、SPICi和MCL。该算法对标注功能类具有较高的几何精度和模块化。与最密切相关的算法MCODE相比，MINE识别的顶部聚类始终具有更高的密度，并且MINE不太可能将重叠模块指定为单个单元。MINE提供了高粒度的少量可调参数，使用户能够微调具有不同拓扑属性的输入网络的聚类结果。MINE是为了应对在高度互联的生物网络中发现高质量基因产物模块的挑战而创建的。该算法允许高度的灵活性和用户自定义的结果与几个可调参数。MINE在识别高模块化模块方面优于几种流行的聚类算法，并在酿酒酵母和秀丽隐杆线虫的蛋白质相互作用网络中获得了良好的功能注释的总体召回率和精度。
Graphical models of network associations are useful for both visualizing and integrating multiple types of association data. Identifying modules, or groups of functionally related gene products, is an important challenge in analyzing biological networks. However, existing tools to identify modules are insufficient when applied to dense networks of experimentally derived interaction data. To address this problem, we have developed an agglomerative clustering method that is able to identify highly modular sets of gene products within highly interconnected molecular interaction networks. MINE outperforms MCODE, CFinder, NEMO, SPICi, and MCL in identifying non-exclusive, high modularity clusters when applied to the C. elegans protein-protein interaction network. The algorithm generally achieves superior geometric accuracy and modularity for annotated functional categories. In comparison with the most closely related algorithm, MCODE, the top clusters identified by MINE are consistently of higher density and MINE is less likely to designate overlapping modules as a single unit. MINE offers a high level of granularity with a small number of adjustable parameters, enabling users to fine-tune cluster results for input networks with differing topological properties. MINE was created in response to the challenge of discovering high quality modules of gene products within highly interconnected biological networks. The algorithm allows a high degree of flexibility and user-customisation of results with few adjustable parameters. MINE outperforms several popular clustering algorithms in identifying modules with high modularity and obtains good overall recall and precision of functional annotations in protein-protein interaction networks from both S. cerevisiae and C. elegans.
登录
查看更多内容
影响因子：
3
作者：
Bader, GD;Hogue, CW
通讯作者：
Hogue, CW
影响因子：
14.9
作者：
Mewes, HW;Frishman, D;Weil, B
通讯作者：
Weil, B
影响因子：
14.9
作者：
Ceol A;Chatr Aryamontri A;Licata L;Peluso D;Briganti L;Perfetto L;Castagnoli L;Cesareni G
通讯作者：
Cesareni G
影响因子：
48
作者：
Simonis, Nicolas;Rual, Jean-Francois;Carvunis, Anne-Ruxandra;Tasan, Murat;Lemmens, Irma;Hirozane-Kishikawa, Tomoko;Hao, Tong;Sahalie, Julie M.;Venkatesan, Kavitha;Gebreab, Fana;Cevik, Sebiha;Klitgord, Niels;Fan, Changyu;Braun, Pascal;Li, Ning;Ayivi-Guedehoussou, Nono;Dann, Elizabeth;Bertin, Nicolas;Szeto, David;Dricot, Ameli;Yildirim, Muhammed A.;Lin, Chenwei;de Smet, Anne-Sophie;Kao, Huey-Ling;Simon, Christophe;Smolyar, Alex;Ahn, Jin Sook;Tewari, Muneesh;Boxem, Mike;Milstein, Stuart;Yu, Haiyuan;Dreze, Matija;Vandenhaute, Jean;Gunsalus, Kristin C.;Cusick, Michael E.;Hill, David E.;Tavernier, Jan;Roth, Frederick P.;Vidal, Marc
通讯作者：
Vidal, Marc
影响因子：
5.8
作者：
Yan, Xifeng;Mehan, Michael R.;Zhou, Xianghong Jasmine
通讯作者：
Zhou, Xianghong Jasmine