Identification of functional modules using network topology and high-throughput data.
Identification of functional modules using network topology and high-throughput data.
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DOI：
10.1186/1752-0509-1-8
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发表时间：
2007-01-26
影响因子：
--
通讯作者：
Shamir R
中科院分区：
文献类型：
--
作者：
Ulitsky I;Shamir R
随着系统生物学的出现，今天的生物学知识通常由网络来表示。这些网络包括调节和代谢网络、蛋白质-蛋白质相互作用网络，以及许多其他网络。与此同时，高通量基因组学和蛋白质组学技术产生了非常大的数据集，这需要复杂的计算分析。通常，对这两种数据类型分别应用不同的分析方法。对网络和高通量信息的综合调查可以通过同时考虑高通量数据的固有特征和拓扑网络属性来提高分析质量。我们为这一挑战描述了一个新的算法框架。我们首先将高通量数据转换为相似性值(例如，通过计算来自微阵列数据的基因表达模式的成对相似性)。然后，给出一个由基因或蛋白质组成的网络，以及其中一些基因或蛋白质之间的相似值，我们就会寻找表现出高相似性的连通子网络(或模块)。我们为这个问题开发了算法，并在酿酒酵母的渗透休克反应网络和人类细胞周期网络上评估了它们的性能。我们证明获得了聚焦的、具有生物意义的和相关的功能模块。与现有算法相比，该方法具有更高的灵敏度和特异度。我们已经证明，我们的方法可以准确地识别功能模块。因此，它承诺在高通量数据分析中非常有用。
With the advent of systems biology, biological knowledge is often represented today by networks. These include regulatory and metabolic networks, protein-protein interaction networks, and many others. At the same time, high-throughput genomics and proteomics techniques generate very large data sets, which require sophisticated computational analysis. Usually, separate and different analysis methodologies are applied to each of the two data types. An integrated investigation of network and high-throughput information together can improve the quality of the analysis by accounting simultaneously for topological network properties alongside intrinsic features of the high-throughput data. We describe a novel algorithmic framework for this challenge. We first transform the high-throughput data into similarity values, (e.g., by computing pairwise similarity of gene expression patterns from microarray data). Then, given a network of genes or proteins and similarity values between some of them, we seek connected sub-networks (or modules) that manifest high similarity. We develop algorithms for this problem and evaluate their performance on the osmotic shock response network in S. cerevisiae and on the human cell cycle network. We demonstrate that focused, biologically meaningful and relevant functional modules are obtained. In comparison with extant algorithms, our approach has higher sensitivity and higher specificity. We have demonstrated that our method can accurately identify functional modules. Hence, it carries the promise to be highly useful in analysis of high throughput data.
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影响因子：
56.9
作者：
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