Mining physical protein-protein interactions from the literature.
Mining physical protein-protein interactions from the literature.
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从文献中挖掘物理蛋白质-蛋白质相互作用
DOI：
10.1186/gb-2008-9-s2-s12
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发表时间：
2008
期刊：
影响因子：
12.3
通讯作者：
Zhu, Xiaoyan
中科院分区：
文献类型：
--
作者：
Huang, Minlie;Ding, Shilin;Wang, Hongning;Zhu, Xiaoyan
研究背景蛋白质-蛋白质相互作用是阐明蛋白质功能和生物学过程的基础。高通量实验技术的发展，如酵母双杂交筛选，产生了爆炸性的数据有关的相互作用。由于人工策展在时间和成本方面都很密集，因此迫切需要文本挖掘工具来促进这些信息的提取。BioCreative（生物学信息提取系统的关键评估）的挑战评估提供了共同的标准和共享的评价标准，使不同的approaches.ResultsDuring的基准评估BioCreative 2006年，我们所有的结果排名前三位。在过滤与物理蛋白质相互作用无关的文章的任务中，我们的方法的精确度为75.07%，召回率为81.07%，AUC（受试者工作特征曲线下的面积）为0.847。在识别蛋白质提及并将提及标准化为分子标识符的任务中，我们的方法在提交的运行中具有竞争力，精确率为34.83%，召回率为24.10%，F1得分为28.5%。在提取蛋白质相互作用对方面，我们的基于轮廓的方法在SwissProt-only子集（精确率= 36.95%，召回率= 32.68%，F1得分= 30.40%）和整个数据集（分别为30.96%，29.35%和26.20%）上具有竞争力。然而，从生物学家的角度来看，这些发现远远不能令人满意。在本报告中提出的错误分析提供了深入了解如何提高性能：四分之三的假阴性是由于蛋白质的标准化问题（532/698），约四分之一是由于正确提取相互作用的问题，为这个system.ConclusionWe提出了一个文本挖掘框架，从文献中提取物理蛋白质-蛋白质相互作用。解决了三个关键问题，即过滤不相关的文章、识别蛋白质名称并将其规范化为分子标识符以及提取蛋白质-蛋白质相互作用。我们的系统是BioCreative 2006基准评估中的前三名。该工具将有助于手动交互策展，并可以大大促进提取蛋白质-蛋白质相互作用的过程。
BackgroundDeciphering physical protein-protein interactions is fundamental to elucidating both the functions of proteins and biological processes. The development of high-throughput experimental technologies such as the yeast two-hybrid screening has produced an explosion in data relating to interactions. Since manual curation is intensive in terms of time and cost, there is an urgent need for text-mining tools to facilitate the extraction of such information. The BioCreative (Critical Assessment of Information Extraction systems in Biology) challenge evaluation provided common standards and shared evaluation criteria to enable comparisons among different approaches.ResultsDuring the benchmark evaluation of BioCreative 2006, all of our results ranked in the top three places. In the task of filtering articles irrelevant to physical protein interactions, our method contributes a precision of 75.07%, a recall of 81.07%, and an AUC (area under the receiver operating characteristic curve) of 0.847. In the task of identifying protein mentions and normalizing mentions to molecule identifiers, our method is competitive among runs submitted, with a precision of 34.83%, a recall of 24.10%, and an F1score of28.5%. In extracting protein interaction pairs, our profile-based method was competitive on the SwissProt-only subset (precision = 36.95%, recall = 32.68%, and F1score = 30.40%) and on the entire dataset (30.96%, 29.35%, and26.20%, respectively). From the biologist's point of view, however, these findings are far from satisfactory. The error analysis presented in this report provides insight into how performance could be improved: three-quarters of false negatives were due to protein normalization problems (532/698), and about one-quarter were due to problems with correctly extracting interactions for this system.ConclusionWe present a text-mining framework to extract physical protein-protein interactions from the literature. Three key issues are addressed, namely filtering irrelevant articles, identifying protein names and normalizing them to molecule identifiers, and extracting protein-protein interactions. Our system is among the top three performers in the benchmark evaluation of BioCreative 2006. The tool will be helpful for manual interaction curation and can greatly facilitate the process of extracting protein-protein interactions.
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