BREAST CANCER NETWORK CENTRALITY

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 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。目前定义新的癌症驱动因素突变的一个挑战是区分大量偶然的乘客突变中的功能驱动因素突变，这些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来重建，包括从各种各样的基因表达模式中确定互信息。利用特征向量中心性的概念，重构的大型网络可以用来确定信令网络中的中心角色。大的邻接矩阵(20,000 x 20,000蛋白质)需要大量的计算资源来计算特征向量中心性，即使在使用专门的算法进行任务时也是如此。R的iggraph包中提供了一个专门的算法，这样的计算可以在内存资源非常大的节点上进行。

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.