Semi-automated curation of protein subcellular localization: a text mining-based approach to Gene Ontology (GO) Cellular Component curation.

Semi-automated curation of protein subcellular localization: a text mining-based approach to Gene Ontology (GO) Cellular Component curation.
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DOI:
10.1186/1471-2105-10-228
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发表时间:
2009-07-21
期刊:
影响因子:
3
通讯作者:
Sternberg PW
Sternberg PW
中科院分区:
生物学4区
文献类型:
--
作者:
Van Auken K;Jaffery J;Chan J;Müller HM;Sternberg PW

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人工管理生物医学文献中的实验数据是一项昂贵且耗时的工作。然而,大多数生物知识库仍然严重依赖于人工管理数据的提取和输入。文本挖掘软件可以半自动或全自动地从文献中检索信息,因此将大大促进人工管理工作。我们使用了由WormBase开发的Textpresso基于分类的信息检索和提取系统,以探索Textpresso如何提高我们将秀丽隐杆线虫蛋白质手工整理到基因本体的细胞成分本体的效率。使用描述已发表文献中定位实验结果的句子训练集,我们生成了三个新的管理任务特定类别(细胞成分、测定术语和动词),其中包含与实验确定的亚细胞定位报告相关的单词和短语。我们将人工整理的结果与Textpresso查询的结果进行了比较,Textpresso查询的文章全文包含三个新类别中的每一个术语加上先前未整理的秀丽隐杆线虫蛋白质的名称,发现与人工整理相比,Textpresso搜索识别出可整理的论文的召回率和准确率分别为79.1%和61.8% (f得分为69.5%)。在这些文档中,Textpresso识别相关句子的召回率和准确率分别为30.3%和80.1% (f得分为44.0%)。从返回的句子中,管理员能够做出66.2%的实验支持的GO Cellular Component注释,精度为97.3% (f值为78.8%)。通过测量基于Textpresso的与手动的管理效率,我们发现Textpresso有潜力将管理效率提高至少8倍,也许多达15倍,考虑到个人管理速度的差异。Textpresso是一个有效的工具,用于提高手工,实验为基础的策展效率。在WormBase中加入基于textpresso的Cellular Component管理管道,使我们能够从严格的手动管理这种数据类型过渡到更有效的计算机辅助验证管道。管理任务特定类别的持续发展将为严重依赖人工管理的基因组数据库提供宝贵的资源。
Manual curation of experimental data from the biomedical literature is an expensive and time-consuming endeavor. Nevertheless, most biological knowledge bases still rely heavily on manual curation for data extraction and entry. Text mining software that can semi- or fully automate information retrieval from the literature would thus provide a significant boost to manual curation efforts. We employ the Textpresso category-based information retrieval and extraction system , developed by WormBase to explore how Textpresso might improve the efficiency with which we manually curate C. elegans proteins to the Gene Ontology's Cellular Component Ontology. Using a training set of sentences that describe results of localization experiments in the published literature, we generated three new curation task-specific categories (Cellular Components, Assay Terms, and Verbs) containing words and phrases associated with reports of experimentally determined subcellular localization. We compared the results of manual curation to that of Textpresso queries that searched the full text of articles for sentences containing terms from each of the three new categories plus the name of a previously uncurated C. elegans protein, and found that Textpresso searches identified curatable papers with recall and precision rates of 79.1% and 61.8%, respectively (F-score of 69.5%), when compared to manual curation. Within those documents, Textpresso identified relevant sentences with recall and precision rates of 30.3% and 80.1% (F-score of 44.0%). From returned sentences, curators were able to make 66.2% of all possible experimentally supported GO Cellular Component annotations with 97.3% precision (F-score of 78.8%). Measuring the relative efficiencies of Textpresso-based versus manual curation we find that Textpresso has the potential to increase curation efficiency by at least 8-fold, and perhaps as much as 15-fold, given differences in individual curatorial speed. Textpresso is an effective tool for improving the efficiency of manual, experimentally based curation. Incorporating a Textpresso-based Cellular Component curation pipeline at WormBase has allowed us to transition from strictly manual curation of this data type to a more efficient pipeline of computer-assisted validation. Continued development of curation task-specific Textpresso categories will provide an invaluable resource for genomics databases that rely heavily on manual curation.
DOI: 10.1186/1471-2105-9-s8-s2
发表时间: 2008-07-22
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影响因子: 3.8
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