Yu Haiyuan, Paccanaro Alberto, Trifonov Valery, Gerstein Mark
Department of Molecular Biophysics and Biochemistry, 266 Whitney Avenue, Yale University, PO Box 208114, New Haven, CT 06520-8285, USA.
Bioinformatics. 2006 Apr 1;22(7):823-9. doi: 10.1093/bioinformatics/btl014. Epub 2006 Feb 2.
Datasets obtained by large-scale, high-throughput methods for detecting protein-protein interactions typically suffer from a relatively high level of noise. We describe a novel method for improving the quality of these datasets by predicting missed protein-protein interactions, using only the topology of the protein interaction network observed by the large-scale experiment. The central idea of the method is to search the protein interaction network for defective cliques (nearly complete complexes of pairwise interacting proteins), and predict the interactions that complete them. We formulate an algorithm for applying this method to large-scale networks, and show that in practice it is efficient and has good predictive performance. More information can be found on our website http://topnet.gersteinlab.org/clique/
Supplementary Materials are available at Bioinformatics online.
通过大规模、高通量方法获得的用于检测蛋白质-蛋白质相互作用的数据集通常存在较高水平的噪声。我们描述了一种新方法，该方法仅利用大规模实验观察到的蛋白质相互作用网络的拓扑结构来预测遗漏的蛋白质-蛋白质相互作用，从而提高这些数据集的质量。该方法的核心思想是在蛋白质相互作用网络中搜索有缺陷的团（成对相互作用蛋白质的近乎完整的复合物），并预测完成它们的相互作用。我们制定了一种将该方法应用于大规模网络的算法，并表明在实践中它是高效的且具有良好的预测性能。更多信息可在我们的网站http://topnet.gersteinlab.org/clique/上找到。
补充材料可在《生物信息学》在线版获取。
