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首页> 外文期刊>IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics >Integration of Genomic Data for Inferring Protein Complexes from Global Protein–Protein Interaction Networks
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Integration of Genomic Data for Inferring Protein Complexes from Global Protein–Protein Interaction Networks

机译：从全球蛋白质-蛋白质相互作用网络推断蛋白质复合物的基因组数据整合。

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摘要

Protein-protein interactions (PPIs) play crucial roles in virtually every aspect of cellular function within an organism. One important objective of modern biology is the extraction of functional modules, such as protein complexes from global protein interaction networks. This paper describes how seven genomic features and four experimental interaction data sets were combined using a Bayesian-networks-based data integration approach to infer PPI networks in yeast. Greater coverage and higher accuracy were achieved than in previous high-throughput studies of PPI networks in yeast. A Markov clustering algorithm was then used to extract protein complexes from the inferred protein interaction networks. The quality of the computed complexes was evaluated using the hand-curated complexes from the Munich Information Center for Protein Sequences database and gene-ontology-driven semantic similarity. The results indicated that, by integrating multiple genomic information sources, a better clustering result was obtained in terms of both statistical measures and biological relevance.
机译：蛋白质-蛋白质相互作用（PPI）实际上在生物体内细胞功能的各个方面都起着至关重要的作用。现代生物学的一个重要目标是从全球蛋白质相互作用网络中提取功能模块，例如蛋白质复合物。本文介绍了如何使用基于贝叶斯网络的数据集成方法组合七个基因组特征和四个实验相互作用数据集，以推断酵母中的PPI网络。与以前的酵母PPI网络的高通量研究相比，可以实现更大的覆盖范围和更高的准确性。然后使用马尔可夫聚类算法从推断的蛋白质相互作用网络中提取蛋白质复合物。使用慕尼黑信息中心蛋白质序列数据库中的手工配制的复合物和基因本体论驱动的语义相似性对计算出的复合物的质量进行了评估。结果表明，通过整合多个基因组信息源，无论是在统计指标还是生物学相关性方面都获得了更好的聚类结果。

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