Lee Sejoon, Lee Eunjung, Lee Kwang H, Lee Doheon
Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea.
Int J Data Min Bioinform. 2011;5(2):131-42. doi: 10.1504/ijdmb.2011.039173.
Network-based methods using molecular interaction networks integrated with gene expression profiles have been proposed to solve problems, which arose from smaller number of samples compared with the large number of predictors. However, previous network-based methods, which have focused only on expression levels of proteins, nodes in the network through the identification of condition-responsive interactions. We propose a novel network-based classification, which focuses on both nodes with discriminative expression levels and edges with Condition-Responsive Correlations (CRCs) across two phenotypes. We found that modules with condition-responsive interactions provide candidate molecular models for diseases and show improved performances compared conventional gene-centric classification methods.
已经提出了基于网络的方法，这些方法使用与基因表达谱整合的分子相互作用网络来解决因样本数量少于预测变量数量而产生的问题。然而，以前基于网络的方法仅关注蛋白质的表达水平，通过识别条件响应相互作用来关注网络中的节点。我们提出了一种新颖的基于网络的分类方法，该方法同时关注具有判别性表达水平的节点和跨两种表型具有条件响应相关性（CRC）的边。我们发现具有条件响应相互作用的模块为疾病提供了候选分子模型，并且与传统的以基因为中心的分类方法相比表现出更好的性能。
