Event-based text mining for biology and functional genomics.
Event-based text mining for biology and functional genomics.
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DOI：
10.1093/bfgp/elu015
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发表时间：
2015-05
影响因子：
4
通讯作者：
Kell DB
中科院分区：
文献类型：
--
作者：
Ananiadou S;Thompson P;Nawaz R;McNaught J;Kell DB
基因组功能的评估需要基因组衍生实体和生化反应之间的映射，而生物医学文献代表了有关生物成分之间反应的丰富信息来源。然而，文献数量的日益快速增长为研究人员及时有效地分离有关感兴趣反应的信息提供了挑战和机遇。为此，生物学领域最近的文本挖掘研究主要集中在从文献中识别和提取“事件”，即生化实体之间关系的分类、结构化表示。功能基因组学分析必然涵盖如此定义的事件。自动事件提取系统有助于开发复杂的语义搜索应用程序，使研究人员能够对提取的事件制定结构化查询，以便指定要检索的反应的确切类型。本文概述了事件提取的最新研究。我们涵盖了训练系统的带注释的语料库、实现最先进性能的系统以及社区共享任务的详细信息，这些任务有助于提高最新系统的质量、覆盖范围和可扩展性。最后，涵盖了事件提取的几个具体应用以及新兴的研究方向。
The assessment of genome function requires a mapping between genome-derived entities and biochemical reactions, and the biomedical literature represents a rich source of information about reactions between biological components. However, the increasingly rapid growth in the volume of literature provides both a challenge and an opportunity for researchers to isolate information about reactions of interest in a timely and efficient manner. In response, recent text mining research in the biology domain has been largely focused on the identification and extraction of ‘events’, i.e. categorised, structured representations of relationships between biochemical entities, from the literature. Functional genomics analyses necessarily encompass events as so defined. Automatic event extraction systems facilitate the development of sophisticated semantic search applications, allowing researchers to formulate structured queries over extracted events, so as to specify the exact types of reactions to be retrieved. This article provides an overview of recent research into event extraction. We cover annotated corpora on which systems are trained, systems that achieve state-of-the-art performance and details of the community shared tasks that have been instrumental in increasing the quality, coverage and scalability of recent systems. Finally, several concrete applications of event extraction are covered, together with emerging directions of research.
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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
影响因子：
3
作者：
Bossy R;Jourde J;Manine AP;Veber P;Alphonse E;van de Guchte M;Bessières P;Nédellec C
通讯作者：
Nédellec C
影响因子：
14.9
作者：
Bader GD;Cary MP;Sander C
通讯作者：
Sander C
影响因子：
3
作者：
Björne J;Ginter F;Salakoski T
通讯作者：
Salakoski T
DOI：
10.1093/bioinformatics/btq180
发表时间：
2010-06-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Björne J;Ginter F;Pyysalo S;Tsujii J;Salakoski T
通讯作者：
Salakoski T