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Predicting disease-related proteins based on clique backbone in protein-protein interaction network

机译:基于蛋白质-蛋白质相互作用网络中的集团骨干预测疾病相关蛋白质

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摘要

Network biology integrates different kinds of data, including physical or functional networks and disease gene sets, to interpret human disease. A clique (maximal complete subgraph) in a protein- protein interaction network is a topological module and possesses inherently biological significance. A disease-related clique possibly associates with complex diseases. Fully identifying disease components in a clique is conductive to uncovering disease mechanisms. This paper proposes an approach of predicting disease proteins based on cliques in a protein-protein interaction network. To tolerate false positive and negative interactions in protein networks, extending cliques and scoring predicted disease proteins with gene ontology terms are introduced to the clique-based method. Precisions of predicted disease proteins are verified by disease phenotypes and steadily keep to more than 95%. The predicted disease proteins associated with cliques can partly complement mapping between genotype and phenotype, and provide clues for understanding the pathogenesis of serious diseases.
机译:网络生物学整合了各种数据,包括物理或功能网络以及疾病基因集,以解释人类疾病。蛋白质-蛋白质相互作用网络中的集团(最大完整子图)是一个拓扑模块,具有内在的生物学意义。与疾病有关的集团可能与复杂的疾病有关。全面识别集团中的疾病成分有助于发现疾病机理。本文提出了一种基于蛋白质-蛋白质相互作用网络中的群体预测疾病蛋白质的方法。为了容忍蛋白质网络中的假阳性和阴性相互作用,将基于基因本体术语的扩展群体和对预测疾病蛋白的评分引入基于群体的方法中。通过疾病表型验证了预测疾病蛋白的准确性,并稳定地保持在95%以上。与群体相关的预测疾病蛋白可以部分补充基因型和表型之间的作图,并为了解严重疾病的发病机理提供线索。

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