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The eFIP system for text mining of protein interaction networks of phosphorylated proteins

机译：用于磷酸化蛋白质的蛋白质相互作用网络的文本挖掘的eFIP系统

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Protein phosphorylation is a central regulatory mechanism in signal transduction involved in most biological processes. Phosphorylation of a protein may lead to activation or repression of its activity, alternative subcellular location and interaction with different binding partners. Extracting this type of information from scientific literature is critical for connecting phosphorylated proteins with kinases and interaction partners, along with their functional outcomes, for knowledge discovery from phosphorylation protein networks. We have developed the Extracting Functional Impact of Phosphorylation (eFIP) text mining system, which combines several natural language processing techniques to find relevant abstracts mentioning phosphorylation of a given protein together with indications of protein–protein interactions (PPIs) and potential evidences for impact of phosphorylation on the PPIs. eFIP integrates our previously developed tools, Extracting Gene Related ABstracts (eGRAB) for document retrieval and name disambiguation, Rule-based LIterature Mining System (RLIMS-P) for Protein Phosphorylation for extraction of phosphorylation information, a PPI module to detect PPIs involving phosphorylated proteins and an impact module for relation extraction. The text mining system has been integrated into the curation workflow of the Protein Ontology (PRO) to capture knowledge about phosphorylated proteins. The eFIP web interface accepts gene/protein names or identifiers, or PubMed identifiers as input, and displays results as a ranked list of abstracts with sentence evidence and summary table, which can be exported in a spreadsheet upon result validation. As a participant in the BioCreative-2012 Interactive Text Mining track, the performance of eFIP was evaluated on document retrieval (F-measures of 78–100%), sentence-level information extraction (F-measures of 70–80%) and document ranking (normalized discounted cumulative gain measures of 93–100% and mean average precision of 0.86). The utility and usability of the eFIP web interface were also evaluated during the BioCreative Workshop. The use of the eFIP interface provided a significant speed-up (～2.5-fold) for time to completion of the curation task. Additionally, eFIP significantly simplifies the task of finding relevant articles on PPI involving phosphorylated forms of a given protein. Database URL: http://proteininformationresource.org/pirwww/iprolink/eFIP.shtml
机译：蛋白质磷酸化是大多数生物过程中涉及的信号转导的主要调节机制。蛋白质的磷酸化可能导致其活性的激活或抑制，亚细胞位置的替代以及与不同结合伴侣的相互作用。从科学文献中提取此类信息对于将磷酸化蛋白与激酶和相互作用伙伴连接以及它们的功能结果，对于从磷酸化蛋白网络中发现知识至关重要。我们已经开发了磷酸化的提取功能影响（eFIP）文本挖掘系统，该系统结合了几种自然语言处理技术，可以找到提及给定蛋白质磷酸化的相关摘要以及蛋白质与蛋白质相互作用（PPI）的迹象以及对蛋白质影响的潜在证据。 PPI的磷酸化。 eFIP集成了我们先前开发的工具，用于文档检索和名称歧义提取的提取基因相关文摘（eGRAB），用于蛋白质磷酸化的基于规则的文献挖掘系统（RLIMS-P）以提取磷酸化信息，用于检测涉及磷酸化蛋白质的PPI的PPI模块以及用于关系提取的影响模块。文本挖掘系统已集成到Protein Ontology（PRO）的管理工作流程中，以捕获有关磷酸化蛋白的知识。 eFIP Web界面接受基因/蛋白质名称或标识符，或PubMed标识符作为输入，并将结果显示为带有句子证据和摘要表的摘要的排名列表，可在结果验证后将其导出到电子表格中。作为BioCreative-2012交互式文本挖掘轨道的参与者，对eFIP的性能进行了评估，包括文档检索（F测度为78-100％），句子级信息提取（F测度为70-80％）和文档排名（归一化折现累计收益度量为93–100％，平均平均精度为0.86）。在BioCreative Workshop期间还评估了eFIP Web界面的实用性和可用性。 eFIP界面的使用大大加快了（约2.5倍）完成管理任务的时间。此外，eFIP大大简化了查找有关PPI的相关文章（涉及给定蛋白质的磷酸化形式）的任务。数据库URL：http://proteininformationresource.org/pirwww/iprolink/eFIP.shtml

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