Unsupervised gene function extraction using semantic vectors
Unsupervised gene function extraction using semantic vectors
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DOI：
10.1093/database/bau084
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发表时间：
2014-09-10
影响因子：
5.8
通讯作者：
Gonzalez, Graciela
中科院分区：
文献类型：
--
作者：
Emadzadeh, Ehsan;Nikfarjam, Azadeh;Gonzalez, Graciela
发现文献中讨论的基因功能是生物医学文献信息抽取的一项重要任务。自动化计算方法可以显著减少手动管理的需要，并提高其他相关IE系统的质量。我们提出了一个开放的IE方法的BioCreative IV GO共享任务（子任务B），专注于寻找基因功能的条款[基因本体论（GO）条款]的不同基因的文章。建议的开放式IE方法是基于分布语义相似性的GO条款。该方法不需要注释数据进行训练，这使得它具有高度的泛化性。在BioCreative-GO共享任务的正式提交中，我们在测试集上实现了0.26的F测量，这是共享任务中七名参与者中第三高的F测量。
Finding gene functions discussed in the literature is an important task of information extraction (IE) from biomedical documents. Automated computational methodologies can significantly reduce the need for manual curation and improve quality of other related IE systems. We propose an open-IE method for the BioCreative IV GO shared task (subtask b), focused on finding gene function terms [Gene Ontology (GO) terms] for different genes in an article. The proposed open-IE approach is based on distributional semantic similarity over the GO terms. The method does not require annotated data for training, which makes it highly generalizable. We achieve an F-measure of 0.26 on the test-set in the official submission for BioCreative-GO shared task, the third highest F-measure among the seven participants in the shared task.