Scalable learning of large networks.
Scalable learning of large networks.
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DOI：
10.1049/iet-syb.2008.0161
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发表时间：
2009-09
影响因子：
2.3
通讯作者：
Lane T
中科院分区：
文献类型：
--
作者：
Roy S;Plis S;Werner-Washburne M;Lane T
从特定条件的微阵列数据推断出的细胞网络可以捕获细胞响应不同环境条件的功能重新布线。不幸的是，许多用于推断细胞网络的算法无法扩展到具有数千个变量的全基因组数据。我们提出了一种用于大型网络可扩展学习的新方法：集群和推理网络（CIN）。 CIN 通过两个步骤学习网络结构：(a) 将变量划分为更小的簇，(b) 每个簇学习网络。我们可以选择重新审视具有较差邻域的变量的聚类分配。具有已知拓扑的网络上的结果表明，CIN 具有显着的速度优势，而没有显着的性能损失。我们将我们的方法应用于葡萄糖饥饿酵母细胞的微阵列概要。与随机图相比，推断网络具有更多数量的代表有意义的生物依赖性的子图。对子图的分析确定了与葡萄糖饥饿下酵母种群的现有信息非常吻合的生物过程，并且还暗示了以前不知道与这些种群相关的新途径。
Cellular networks inferred from condition-specific microarray data can capture the functional rewiring of cells in response to different environmental conditions. Unfortunately, many algorithms for inferring cellular networks do not scale to whole-genome data with thousands of variables. We propose a novel approach for scalable learning of large networks: Cluster and Infer Networks (CIN). CIN learns network structures in two steps: (a) partition variables into smaller clusters, (b) learn networks per cluster. We optionally revisit the cluster assignment of variables with poor neighborhoods. Results on networks with known topologies suggest that CIN has substantial speed benefits, without substantial performance loss. We applied our approach to microarray compendia of glucose-starved yeast cells. The inferred networks had significantly higher number of subgraphs representing meaningful biological dependencies than random graphs. Analysis of subgraphs identified biological processes that agreed well with existing information about yeast populations under glucose starvation, and also implicated novel pathways that were previously not known to be associated with these populations.
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影响因子：
5.8
作者：
Lee, PH;Lee, D
通讯作者：
Lee, D
影响因子：
56.9
作者：
Milo, R;Shen-Orr, S;Alon, U
通讯作者：
Alon, U
DOI：
10.1083/jcb.200604072
发表时间：
2006-07-03
期刊：
The Journal of cell biology
影响因子：
--
作者：
Allen C;Büttner S;Aragon AD;Thomas JA;Meirelles O;Jaetao JE;Benn D;Ruby SW;Veenhuis M;Madeo F;Werner-Washburne M
通讯作者：
Werner-Washburne M
影响因子：
2.1
作者：
Inoue, Lurdes Y. T.;Neira, Mauricio;Etzioni, Ruth
通讯作者：
Etzioni, Ruth
影响因子：
3.3
作者：
Aragon, Anthony D.;Rodriguez, Angelina L.;Werner-Washburne, Margaret
通讯作者：
Werner-Washburne, Margaret