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首页> 外文期刊>BMC Medical Genomics >Integrating PPI datasets with the PPI data from biomedical literature for protein complex detection
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Integrating PPI datasets with the PPI data from biomedical literature for protein complex detection

机译：将PPI数据集与生物医学文献中的PPI数据集成在一起以进行蛋白质复合物检测

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Background Protein complexes are important for understanding principles of cellular organization and function. High-throughput experimental techniques have produced a large amount of protein-protein interactions (PPIs), making it possible to predict protein complexes from protein-protein interaction networks. On the other hand, the rapidly growing biomedical literature provides a significantly large and readily available source of interaction data, which can be integrated into the protein network for better complex detection performance. Methods We present an approach of integrating PPI datasets with the PPI data from biomedical literature for protein complex detection. The approach applies a sophisticated natural language processing system, PPIExtractor, to extract PPI data from biomedical literature. These data are then integrated into the PPI datasets for complex detection. Results The experimental results of the state-of-the-art complex detection method, ClusterONE, on five yeast PPI datasets verify our method's effectiveness: compared with the original PPI datasets, the average improvements of 3.976 and 5.416 percentage units in the maximum matching ratio (MMR) are achieved on the new networks using the MIPS and SGD gold standards, respectively. In addition, our approach also proves to be effective for three other complex detection algorithms proposed in recent years, i.e. CMC, COACH and RRW. Conclusions The rapidly growing biomedical literature provides a significantly large, readily available and relatively accurate source of interaction data, which can be integrated into the protein network for better protein complex detection performance.
机译：背景蛋白复合物对于理解细胞组织和功能原理很重要。高通量实验技术已经产生了大量的蛋白质-蛋白质相互作用（PPI），从而可以从蛋白质-蛋白质相互作用网络预测蛋白质复合物。另一方面，迅速增长的生物医学文献提供了非常大且易于获得的交互作用数据源，可以将其集成到蛋白质网络中以获得更好的复杂检测性能。方法我们提出了一种将PPI数据集与生物医学文献中的PPI数据集成在一起以进行蛋白质复合物检测的方法。该方法应用了复杂的自然语言处理系统PPIExtractor，以从生物医学文献中提取PPI数据。然后将这些数据集成到PPI数据集中进行复杂检测。结果最新的复杂检测方法ClusterONE在五个酵母PPI数据集上的实验结果证明了该方法的有效性：与原始PPI数据集相比，最大匹配率平均提高了3.976和5.416个百分点（MMR）分别在新的网络上使用MIPS和SGD黄金标准实现。此外，我们的方法还被证明对近年来提出的其他三种复杂的检测算法（即CMC，COACH和RRW）有效。结论迅速增长的生物医学文献提供了相当大的，易于获得的和相对准确的相互作用数据源，可以将其整合到蛋白质网络中以实现更好的蛋白质复合物检测性能。

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