共查询到18条相似文献,搜索用时 375 毫秒
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蛋白质网络聚类是识别功能模块的重要手段,不仅有利于理解生物系统的组织结构,对预测蛋白质功能也具有重要的意义。针对目前蛋白质网络聚类算法缺乏有效分析软件的事实,本文设计并实现了一个新的蛋白质网络聚类算法分析平台ClusterE。该平台实现了查全率、查准率、敏感性、特异性、功能富集分析等聚类评估方法,并且集成了FAG-EC、Dpclus、Monet、IPC-MCE、IPCA等聚类算法,不仅可以对蛋白质网络聚类分析结果进行可视化,并且可以在不同聚类分析指标下对多个聚类算法进行可视化比较与分析。该平台具有良好的扩展性,其中聚类算法以及聚类评估方法都是以插件形式集成到系统中。 相似文献
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PNmerger: 一个整合生物学通路和蛋白质相互作用网络的Cytoscape插件 总被引:1,自引:0,他引:1
Cytoscape是一个广泛应用于分子相互作用网络可视化的软件.发展了一个基于java的Cytoscape插件PNmerger.对于一个蛋白质相互作用网络,PNmerger能够使用KEGG数据库中的通路信息自动注释网络中的蛋白质.并通过网络和通路的比较发现网络中已知的通路元件,预测可能的通路元件及通路交联元件.该软件可以可视化网络中存在的通路模块,并将连接不同通路间的潜在交联元件显示出来.PNmerger软件能够有效地帮助实验人员发现网络中重要的功能线索,帮助实验人员进行实验设计.用户可以通过网站http://www.hupo.org.cn/PNmerger下载PNmerger插件. 相似文献
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基于蛋白质网络功能模块的蛋白质功能预测 总被引:1,自引:0,他引:1
在破译了基因序列的后基因组时代,随着系统生物学实验的快速发展,产生了大量的蛋白质相互作用数据,利用这些数据寻找功能模块及预测蛋白质功能在功能基因组研究中具有重要意义.打破了传统的基于蛋白质间相似度的聚类模式,直接从蛋白质功能团的角度出发,考虑功能团间的一阶和二阶相互作用,提出了模块化聚类方法(MCM),对实验数据进行聚类分析,来预测模块内未知蛋白质的功能.通过超几何分布P值法和增、删、改相互作用的方法对聚类结果进行预测能力分析和稳定性分析.结果表明,模块化聚类方法具有较高的预测准确度和覆盖率,有很好的容错性和稳定性.此外,模块化聚类分析得到了一些具有高预测准确度的未知蛋白质的预测结果,将会对生物实验有指导意义,其算法对其他具有相似结构的网络也具有普遍意义. 相似文献
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细胞生物过程具有时序动态性,蛋白质功能模块是驱动细胞生物过程的功能单位。为了蛋白质功能模块识别,本文将细胞生物过程建模为动态时序表达相关蛋白质相互作用网络(DTEPIN);构建子块矩阵以表示动态时序表达相关蛋白质相互作用网络;利用子块矩阵特殊性,分析时空复杂度和并行性;优化设计马尔可夫聚类算法,以识别动态时序表达相关蛋白质相互作用网络中的蛋白质功能模块。为了支持基于子块矩阵马尔可夫聚类过程,本文运用图形处理器并行计算矩阵乘积。实验结果表明,与已有同类算法相比,所设计算法识别的蛋白质功能模块,统计匹配质量更高且精确匹配数量更多。 相似文献
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图聚类用于蛋白质分类问题可以获得较好结果,其前提是将蛋白质之间复杂的相互关系转化为适当的相似性网络作为图聚类分类的输入数据。本文提出一种基于BLAST检索的相似性网络构建方法,从目标蛋白质序列出发,通过若干轮次的BLAST检索逐步从数据库中提取与目标蛋白质直接或间接相关的序列,构成关联集。关联集中序列之间的相似性关系即相似性网络,可作为图聚类算法的分类依据。对Pfam数据库中依直接相似关系难以正确分类的蛋白质的计算表明,按本文方法构建的相似性网络取得了比较满意的结果。 相似文献
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关键蛋白质是指那些在蛋白质相互作用网络中承担重要作用、移除后会使蛋白质复合物功能丧失并导致生物无法存活的节点。随着蛋白质数据库的不断完善和高通量技术的发展,使得通过计算方法的关键蛋白预测得到广泛应用。针对目前软件多为桌面应用程序、用户难以迅速适应的情况,本文设计并实现了一个基于WEB的关键蛋白质预测平台EssentialProtein Finder(EP Finder)。该平台集成了DC、BC、CC、EC、LAC、SC和NC7种关键蛋白质预测算法,还提供包含SN、SP、PPV、NPV、ACC、F和折刀曲线图在内的7种评估方法。平台对蛋白质网络图、算法运行及评估结果提供了可视化展示。该平台具有良好的扩展性。 相似文献
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基于质谱的非标记定量方法能够对复杂蛋白质组进行规模化分析,同时,在定量分析的基础上理解和解释蛋白质组的功能和相互作用关系更有意义.这需要建立一种有效的兼容定量和定性分析结果的方法.针对这一需求,本文首先借鉴了NSAF(normalized spectral abundance factor)算法采用肽段计数对蛋白质组数据进行定量,进一步结合共享肽对该方法进行优化.以此为基础,通过g:Profiler获取海量蛋白质组的功能注释信息,在定量分析的过程中,同步实现了对蛋白质组数据的功能性分析.本文选择来自人心脏、小鼠心脏、小鼠肝脏的三组线粒体蛋白质组数据对该方法进行验证,按照功能性分析将三组数据划分为若干功能组或信号通路,并进行相关性、功能聚类以及电子传递链分析.结果表明,结合共享肽的优化算法克服了对低丰度蛋白质的错误估计,提高了非标记定量的准确性.同时,结合生物医学知识的分析方法解释了蛋白质组的功能和相互作用关系,为差异比较蛋白质组学、疾病蛋白质组学以及功能蛋白质组学等组学研究提供了新的方法. 相似文献
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MDB: the Metalloprotein Database and Browser at The Scripps Research Institute 总被引:3,自引:0,他引:3 下载免费PDF全文
Jesus M. Castagnetto Sean W. Hennessy Victoria A. Roberts Elizabeth D. Getzoff John A. Tainer Michael E. Pique 《Nucleic acids research》2002,30(1):379-382
The Metalloprotein Database and Browser (MDB; http://metallo.scripps.edu) at The Scripps Research Institute is a web-accessible resource for metalloprotein research. It offers the scientific community quantitative information on geometrical parameters of metal-binding sites in protein structures available from the Protein Data Bank (PDB). The MDB also offers analytical tools for the examination of trends or patterns in the indexed metal-binding sites. A user can perform interactive searches, metal-site structure visualization (via a Java applet), and analysis of the quantitative data by accessing the MDB through a web browser without requiring an external application or platform-dependent plugin. The MDB also has a non-interactive interface with which other web sites and network-aware applications can seamlessly incorporate data or statistical analysis results from metal-binding sites. The information contained in the MDB is periodically updated with automated algorithms that find and index metal sites from new protein structures released by the PDB. 相似文献
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SUMMARY: DrugViz is a Cytoscape plugin that is designed to visualize and analyze small molecules within the framework of the interactome. DrugViz can import drug-target network information in an extended SIF file format to Cytoscape and display the two-dimensional (2D) structures of small molecule nodes in a unified visualization environment. It also can identify small molecule nodes by means of three different 2D structure searching methods, namely isomorphism, substructure and fingerprint-based similarity searches. After selections, users can furthermore conduct a two-side clustering analysis on drugs and targets, which allows for a detailed analysis of the active compounds in the network, and elucidate relationships between these drugs and targets. DrugViz represents a new tool for the analysis of data from chemogenomics, metabolomics and systems biology. AVAILABILITY: DrugViz and data set used in Application are freely available for download at http://202.127.30.184:8080/software.html. 相似文献
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Lionel Spinelli Philippe Gambette Charles E. Chapple Benoît Robisson Anaïs Baudot Henri Garreta Laurent Tichit Alain Guénoche Christine Brun 《Bio Systems》2013
Background and scope
Large networks, such as protein interaction networks, are extremely difficult to analyze as a whole. We developed Clust&See, a Cytoscape plugin dedicated to the identification, visualization and analysis of clusters extracted from such networks.Implementation and performance
Clust&See provides the ability to apply three different, recently developed graph clustering algorithms to networks and to visualize: (i) the obtained partition as a quotient graph in which nodes correspond to clusters and (ii) the obtained clusters as their corresponding subnetworks. Importantly, tools for investigating the relationships between clusters and vertices as well as their organization within the whole graph are supplied. 相似文献15.
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MOTIVATION: Microarrays have become a central tool in biological research. Their applications range from functional annotation to tissue classification and genetic network inference. A key step in the analysis of gene expression data is the identification of groups of genes that manifest similar expression patterns. This translates to the algorithmic problem of clustering genes based on their expression patterns. RESULTS: We present a novel clustering algorithm, called CLICK, and its applications to gene expression analysis. The algorithm utilizes graph-theoretic and statistical techniques to identify tight groups (kernels) of highly similar elements, which are likely to belong to the same true cluster. Several heuristic procedures are then used to expand the kernels into the full clusters. We report on the application of CLICK to a variety of gene expression data sets. In all those applications it outperformed extant algorithms according to several common figures of merit. We also point out that CLICK can be successfully used for the identification of common regulatory motifs in the upstream regions of co-regulated genes. Furthermore, we demonstrate how CLICK can be used to accurately classify tissue samples into disease types, based on their expression profiles. Finally, we present a new java-based graphical tool, called EXPANDER, for gene expression analysis and visualization, which incorporates CLICK and several other popular clustering algorithms. AVAILABILITY: http://www.cs.tau.ac.il/~rshamir/expander/expander.html 相似文献
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FunMod: A Cytoscape Plugin for Identifying Functional Modules in Undirected Protein–Protein Networks
The characterization of the interacting behaviors of complex biological systems is a primary objective in protein–protein network analysis and computational biology. In this paper we present FunMod, an innovative Cytoscape version 2.8 plugin that is able to mine undirected protein–protein networks and to infer sub-networks of interacting proteins intimately correlated with relevant biological pathways. This plugin may enable the discovery of new pathways involved in diseases. In order to describe the role of each protein within the relevant biological pathways, FunMod computes and scores three topological features of the identified sub-networks. By integrating the results from biological pathway clustering and topological network analysis, FunMod proved to be useful for the data interpretation and the generation of new hypotheses in two case studies. 相似文献
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Audenaert P Van Parys T Brondel F Pickavet M Demeester P Van de Peer Y Michoel T 《Bioinformatics (Oxford, England)》2011,27(11):1587-1588
SUMMARY: Network motifs in integrated molecular networks represent functional relationships between distinct data types. They aggregate to form dense topological structures corresponding to functional modules which cannot be detected by traditional graph clustering algorithms. We developed CyClus3D, a Cytoscape plugin for clustering composite three-node network motifs using a 3D spectral clustering algorithm. AVAILABILITY: Via the Cytoscape plugin manager or http://bioinformatics.psb.ugent.be/software/details/CyClus3D. 相似文献