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Cluster analysis of networks generated through homology: automatic identification of important protein communities involved in cancer metastasis

机译:通过同源性生成的网络的聚类分析:自动识别参与癌症转移的重要蛋白质群落

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摘要

BackgroundProtein-protein interactions have traditionally been studied on a small scale, using classical biochemical methods to investigate the proteins of interest. More recently large-scale methods, such as two-hybrid screens, have been utilised to survey extensive portions of genomes. Current high-throughput approaches have a relatively high rate of errors, whereas in-depth biochemical studies are too expensive and time-consuming to be practical for extensive studies. As a result, there are gaps in our knowledge of many key biological networks, for which computational approaches are particularly suitable.
机译:背景技术传统上,使用经典的生化方法来研究目标蛋白质,是对蛋白质-蛋白质相互作用进行了小规模研究。最近,诸如两杂交筛选的大规模方法已被用于调查基因组的广泛部分。当前的高通量方法具有相对较高的错误率,而深入的生化研究过于昂贵且耗时,无法进行广泛的研究。结果,我们在许多关键生物网络的知识上存在空白,因此计算方法特别适合。

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