School of Mathematical Sciences, Dalian University of Technology, Dalian 116024, China.
BMC Bioinformatics. 2012 Jun 11;13 Suppl 9(Suppl 9):S3. doi: 10.1186/1471-2105-13-S9-S3.
Identifying gene regulatory network (GRN) from time course gene expression data has attracted more and more attentions. Due to the computational complexity, most approaches for GRN reconstruction are limited on a small number of genes and low connectivity of the underlying networks. These approaches can only identify a single network for a given set of genes. However, for a large-scale gene network, there might exist multiple potential sub-networks, in which genes are only functionally related to others in the sub-networks.
We propose the network and community identification (NCI) method for identifying multiple subnetworks from gene expression data by incorporating community structure information into GRN inference. The proposed algorithm iteratively solves two optimization problems, and can promisingly be applied to large-scale GRNs. Furthermore, we present the efficient Block PCA method for searching communities in GRNs.
The NCI method is effective in identifying multiple subnetworks in a large-scale GRN. With the splitting algorithm, the Block PCA method shows a promosing attempt for exploring communities in a large-scale GRN.
从时间序列基因表达数据中识别基因调控网络（GRN）引起了越来越多的关注。由于计算复杂度的限制，大多数 GRN 重建方法仅限于少数基因和底层网络的低连通性。这些方法只能为给定的一组基因识别单个网络。然而，对于大规模的基因网络，可能存在多个潜在的子网络，其中基因仅与子网络中的其他基因在功能上相关。
我们提出了网络和社区识别（NCI）方法，通过将社区结构信息纳入 GRN 推断来从基因表达数据中识别多个子网络。所提出的算法迭代地解决两个优化问题，并且可以有希望地应用于大规模 GRNs。此外，我们提出了有效的块主成分分析（Block PCA）方法来搜索 GRNs 中的社区。
NCI 方法可有效地识别大规模 GRN 中的多个子网。通过分裂算法，Block PCA 方法为探索大规模 GRN 中的社区提供了有希望的尝试。
