Cataloging the biomedical world of pain through semi-automated curation of molecular interactions.
Cataloging the biomedical world of pain through semi-automated curation of molecular interactions.
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DOI：
10.1093/database/bat033
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Nenadic G
中科院分区：
文献类型：
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作者：
Jamieson DG;Roberts PM;Robertson DL;Sidders B;Nenadic G
大量的生物医学文献及其不断扩展给研究人员带来了许多挑战，他们需要结构化的发现来跟上并分析与其感兴趣领域相关的分子机制。通过将文献内容构建到特定于主题的机器可读数据库中，来自多篇文章的聚合数据可用于推断趋势，这些趋势可以与来自主题无关资源的类似发现进行比较和对比。我们的研究提出了一种通用程序，通过使用文本挖掘来辅助手动管理，半自动创建自定义特定主题分子相互作用数据库。我们应用该程序来捕获“疼痛”背后的分子事件，这是一种复杂的现象，具有巨大的社会负担和未得到满足的医疗需求。我们描述了如何使用现有的文本挖掘解决方案来构建特定于疼痛的语料库，从中提取分子事件，为提取的事件添加上下文并评估其相关性。疼痛特异性语料库包含来自 Medline 和 PubMed Central 的 765 692 个文档，我们从中提取了 356 499 个独特的标准化分子事件，其中包括由 BioContext 提供的 261 438 个单一蛋白质事件和 93 271 个分子相互作用。事件链用否定、推测、解剖学、基因本体术语、突变、疼痛和疾病相关性进行注释，这些共同提供了事件链如何与疼痛相关的详细见解。提取的关系在维基平台 (wiki-pain.org) 中可视化，该平台能够有效地手动管理和探索疼痛背后的分子机制。对按疼痛相关性排序的 1500 个分组事件链进行管理，揭示了 613 个准确提取的独特分子相互作用，这些相互作用在未来可用于研究与疼痛相关的潜在机制。我们的方法表明，将现有的文本挖掘工具与特定领域的术语和基于 wiki 的可视化相结合可以促进分子相互作用的快速管理以创建自定义数据库。数据库网址：•••
The vast collection of biomedical literature and its continued expansion has presented a number of challenges to researchers who require structured findings to stay abreast of and analyze molecular mechanisms relevant to their domain of interest. By structuring literature content into topic-specific machine-readable databases, the aggregate data from multiple articles can be used to infer trends that can be compared and contrasted with similar findings from topic-independent resources. Our study presents a generalized procedure for semi-automatically creating a custom topic-specific molecular interaction database through the use of text mining to assist manual curation. We apply the procedure to capture molecular events that underlie ‘pain’, a complex phenomenon with a large societal burden and unmet medical need. We describe how existing text mining solutions are used to build a pain-specific corpus, extract molecular events from it, add context to the extracted events and assess their relevance. The pain-specific corpus contains 765 692 documents from Medline and PubMed Central, from which we extracted 356 499 unique normalized molecular events, with 261 438 single protein events and 93 271 molecular interactions supplied by BioContext. Event chains are annotated with negation, speculation, anatomy, Gene Ontology terms, mutations, pain and disease relevance, which collectively provide detailed insight into how that event chain is associated with pain. The extracted relations are visualized in a wiki platform (wiki-pain.org) that enables efficient manual curation and exploration of the molecular mechanisms that underlie pain. Curation of 1500 grouped event chains ranked by pain relevance revealed 613 accurately extracted unique molecular interactions that in the future can be used to study the underlying mechanisms involved in pain. Our approach demonstrates that combining existing text mining tools with domain-specific terms and wiki-based visualization can facilitate rapid curation of molecular interactions to create a custom database. Database URL: •••