BREAST CANCER NETWORK CENTRALITY

这个子项目是许多研究子项目中的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。列出的机构是 中心，不一定是研究者的机构。 已知的癌症驱动突变发生在位于癌细胞信号传导网络中心位置的基因中。目前定义新的癌症驱动突变的挑战是在大量偶然的乘客突变中区分功能驱动突变，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重建，包括从各种基因表达模式中确定互信息。使用特征向量中心性的概念，大型重构网络可以用于确定信令网络中的中心参与者。大型邻接矩阵（20，000 x 20，000个蛋白质）需要大量的计算资源来计算特征向量中心性，即使使用专门的算法也是如此。R的igraph包中提供了一个专门的算法，可以在具有极大内存资源的节点上进行这样的计算。

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.