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Graphlet alignment in protein interaction networks

机译:蛋白质相互作用网络中的小图比对

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With the increased availability of genome-scale data, it becomes possible to study functional relationships of genes across multiple biological networks. While most previous approaches for studying conservation of patterns in networks are through the application of network alignment algorithms or the identification of network motifs, we show that it is possible to exhaustively enumerate all graphlet alignments, which consist of subgraphs from each network that share a common topology and contain homologous proteins at the same position in the topology. We show that our algorithm is able to cover significantly more proteins than previous network alignment algorithms while achieving comparable specificity and higher sensitivity with respect to functional enrichment.
机译:随着基因组规模数据可用性的提高,研究跨多个生物网络的基因的功能关系成为可能。尽管大多数研究网络中模式守恒的方法都是通过应用网络对齐算法或网络图案识别,但我们表明,有可能穷举所有小图对齐方式,这些小图对齐方式包括来自每个网络的子图,这些子图共享一个共同点。拓扑结构,并且在拓扑结构中的相同位置包含同源蛋白。我们证明,与以前的网络比对算法相比,我们的算法能够覆盖更多的蛋白质,同时在功能富集方面达到可比的特异性和更高的灵敏度。

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