Overview of the protein-protein interaction annotation extraction task of BioCreative II.
Overview of the protein-protein interaction annotation extraction task of BioCreative II.
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BioCreative II 蛋白质-蛋白质相互作用注释提取任务概述。
DOI：
10.1186/gb-2008-9-s2-s4
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发表时间：
2008
期刊：
影响因子：
12.3
通讯作者：
Valencia, Alfonso
中科院分区：
文献类型：
--
作者：
Krallinger, Martin;Leitner, Florian;Rodriguez-Penagos, Carlos;Valencia, Alfonso
生物医学文献是手工蛋白质-蛋白质相互作用注释的主要信息来源。文本挖掘系统已经被实现来从文章中提取二进制蛋白质相互作用，但是缺少不同技术之间的全面比较以及与人工策展的比较。我们设计了一个社区挑战，BioCreative II蛋白质相互作用（PPI）任务，基于手动蛋白质相互作用注释工作流程的主要步骤。它分为四个不同的子任务：（a）蛋白质相互作用相关的文章检测;（B）蛋白质相互作用对的提取和标准化;（c）检索所使用的相互作用检测方法;（d）检索提供蛋白质相互作用证据的实际文本段落。共有26个团队提交了至少一个拟议子任务的运行。在交互文章检测子任务中，得分最高的团队达到了0.78的F分数。在相互作用对提取和映射到SwissProt中，获得了0.37的精度（召回率为0.33）。为了将文章与实验性交互检测方法相关联，实现了0.65的F分数。至于在全文文章中最好地总结给定蛋白质相互作用的PPI段落的检索，其中一次运行返回的提交中有19%对应于策展人选择的句子。策展人只提取了最能概括给定交互的段落，这意味着许多自动提取的段落可能包含交互信息，但并不对应于信息量最大的句子。BioCreative II PPI任务是第一次尝试比较PPI提取管道每个基本步骤的文本挖掘工具的性能。确定的挑战范围从全文格式转换的文章，以检测相互作用蛋白质对，然后将它们链接到他们的数据库记录的困难的问题。当使用单个（并且可能不完整）参考数据库进行蛋白质标准化时，或者当将搜索相互作用蛋白质限制为在单个句子内共现时，当提及可能跨越相邻句子时，也遇到了一些限制。最后，区分新的、实验验证的交互（注释相关）和先前已知的交互增加了这些任务的额外复杂性。
The biomedical literature is the primary information source for manual protein-protein interaction annotations. Text-mining systems have been implemented to extract binary protein interactions from articles, but a comprehensive comparison between the different techniques as well as with manual curation was missing. We designed a community challenge, the BioCreative II protein-protein interaction (PPI) task, based on the main steps of a manual protein interaction annotation workflow. It was structured into four distinct subtasks related to: (a) detection of protein interaction-relevant articles; (b) extraction and normalization of protein interaction pairs; (c) retrieval of the interaction detection methods used; and (d) retrieval of actual text passages that provide evidence for protein interactions. A total of 26 teams submitted runs for at least one of the proposed subtasks. In the interaction article detection subtask, the top scoring team reached an F-score of 0.78. In the interaction pair extraction and mapping to SwissProt, a precision of 0.37 (with recall of 0.33) was obtained. For associating articles with an experimental interaction detection method, an F-score of 0.65 was achieved. As for the retrieval of the PPI passages best summarizing a given protein interaction in full-text articles, 19% of the submissions returned by one of the runs corresponded to curator-selected sentences. Curators extracted only the passages that best summarized a given interaction, implying that many of the automatically extracted ones could contain interaction information but did not correspond to the most informative sentences. The BioCreative II PPI task is the first attempt to compare the performance of text-mining tools specific for each of the basic steps of the PPI extraction pipeline. The challenges identified range from problems in full-text format conversion of articles to difficulties in detecting interactor protein pairs and then linking them to their database records. Some limitations were also encountered when using a single (and possibly incomplete) reference database for protein normalization or when limiting search for interactor proteins to co-occurrence within a single sentence, when a mention might span neighboring sentences. Finally, distinguishing between novel, experimentally verified interactions (annotation relevant) and previously known interactions adds additional complexity to these tasks.
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影响因子：
12.3
作者：
Alex B;Grover C;Haddow B;Kabadjov M;Klein E;Matthews M;Tobin R;Wang X
通讯作者：
Wang X
从文献中挖掘物理蛋白质-蛋白质相互作用
影响因子：
12.3
作者：
Huang, Minlie;Ding, Shilin;Wang, Hongning;Zhu, Xiaoyan
通讯作者：
Zhu, Xiaoyan
影响因子：
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作者：
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通讯作者：
Hovig, E
影响因子：
12.3
作者：
Rinaldi, Fabio;Kappeler, Thomas;Vachon, Therese
通讯作者：
Vachon, Therese
影响因子：
14.9
作者：
Chatr-aryamontri, Andrew;Ceol, Arnaud;Palazzi, Luisa Montecchi;Nardelli, Giuliano;Schneider, Maria Victoria;Castagnoli, Luisa;Cesareni, Gianni
通讯作者：
Cesareni, Gianni