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Overview of the Cancer Genetics and Pathway Curation tasks of BioNLP Shared Task 2013

机译:BioNLP共享任务2013的癌症遗传学和途径治疗任务概述

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Background Since their introduction in 2009, the BioNLP Shared Task events have been instrumental in advancing the development of methods and resources for the automatic extraction of information from the biomedical literature. In this paper, we present the Cancer Genetics (CG) and Pathway Curation (PC) tasks, two event extraction tasks introduced in the BioNLP Shared Task 2013. The CG task focuses on cancer, emphasizing the extraction of physiological and pathological processes at various levels of biological organization, and the PC task targets reactions relevant to the development of biomolecular pathway models, defining its extraction targets on the basis of established pathway representations and ontologies. Results Six groups participated in the CG task and two groups in the PC task, together applying a wide range of extraction approaches including both established state-of-the-art systems and newly introduced extraction methods. The best-performing systems achieved F-scores of 55% on the CG task and 53% on the PC task, demonstrating a level of performance comparable to the best results achieved in similar previously proposed tasks. Conclusions The results indicate that existing event extraction technology can generalize to meet the novel challenges represented by the CG and PC task settings, suggesting that extraction methods are capable of supporting the construction of knowledge bases on the molecular mechanisms of cancer and the curation of biomolecular pathway models. The CG and PC tasks continue as open challenges for all interested parties, with data, tools and resources available from the shared task homepage.
机译:背景技术自2009年推出以来,BioNLP共享任务事件一直在推动开发方法和资源以从生物医学文献中自动提取信息方面发挥了作用。在本文中,我们介绍了癌症遗传学(CG)和通路治疗(PC)任务,这是在BioNLP Shared Task 2013中引入的两个事件提取任务。CG任务着重于癌症,强调了各个层次的生理和病理过程的提取生物任务,PC任务以与生物分子途径模型的开发相关的反应为目标,并基于已建立的途径表示形式和本体定义其提取目标。结果六个小组参加了CG任务,两个小组参加了PC任务,一起使用了广泛的提取方法,包括已建立的最新系统和新引入的提取方法。表现最佳的系统在CG任务上的F分数达到55%,在PC任务上的F分数达到53%,表现出的性能水平与以前提出的类似任务所能达到的最佳结果相当。结论结果表明,现有的事件提取技术可以推广以应对CG和PC任务设置所代表的新挑战,这表明提取方法能够支持有关癌症分子机制和生物分子途径调控的知识基础的构建。楷模。 CG和PC任务继续是所有相关方面临的开放挑战,其共享任务主页上提供了数据,工具和资源。

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