BREAST CANCER NETWORK CENTRALITY

乳腺癌网络中心性

• 批准号: 7723338
• 负责人: NICHOLAS Joseph SCHORK
• 金额: $ 0.05万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-01 至 2009-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7723338
• 关键词: Algorithms; Automobile Driving; Computer Retrieval of Information on Scientific Projects Database; Computing Methodologies; Funding; Gene Expression; Genes; Grant; Institution; Malignant Neoplasms; Memory; Mutation; Numbers; Pattern; Positioning Attribute; Proteins; Research; Research Personnel; Resources; Signal Transduction; Source; United States National Institutes of Health; cancer cell; concept; malignant breast neoplasm

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Known cancer driving mutations occur in genes lying in central positions of the cancer cell signaling network. A current challenge in defining new cancer driver mutations is distinguishing between functional driver mutations among a large number of incidental passenger mutations with no functional significance in terms of tumorogenesis. Cell signaling networks can be reconstructed by various computational methods, including determination of mutual information from a wide variety of gene expression patterns. The large reconstructed network can be used to determine central players in the signaling network, using the concept of eigenvector centrality. The large adjacency matrix (20,000 x 20,000 proteins), requires significant computational resources for the calculation of eigenvecetor centrality, even when using specialized algorithms for the task. A specialized algorithm is available in the igraph package of R, and such a calculation can be carried out on a node with an extremely large memory resource.

该副本是利用众多研究子项目之一 由NIH/NCRR资助的中心赠款提供的资源。子弹和 调查员（PI）可能已经从其他NIH来源获得了主要资金， 因此，可以在其他清晰的条目中表示。列出的机构是 对于中心，这不一定是调查员的机构。 已知的癌症驱动突变发生在位于癌细胞信号网络中心位置的基因中。当前定义新的癌症驱动突变的挑战​​是区分大量偶然乘客突变之间的功能驱动突变，在肿瘤发生方面没有功能意义。细胞信号网络可以通过各种计算方法重建，包括从多种基因表达模式中确定相互信息。大型重建网络可用于使用特征向量中心性的概念来确定信号网络中的中央播放器。大型邻接矩阵（20,000 x 20,000蛋白）也需要大量的计算资源来计算特征vecetor中心性，即使在任务中使用专门的算法。 R的IGRAPH软件包中提供了专门的算法，可以在具有极大的存储器资源的节点上进行此类计算。