• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Computational Biology and Bioinformatics, IEEE/ACM Transactions on >Gene Name Disambiguation UsingMulti-Scope Species Detection
【24h】

Gene Name Disambiguation UsingMulti-Scope Species Detection

机译：使用多范围物种检测消除基因名称歧义

获取原文
获取原文并翻译 | 示例
       
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Species detection is an important topic in the text mining field. According to the importance of the research topics (e.g., species assignment to genes and document focus species detection), some studies are dedicated to an individual topic. However, no researcher to date has discussed species detection as a general problem. Therefore, we developed a multi-scope species detection model to identify the focus species for different scopes (i.e., gene mention, sentence, paragraph, and global scope of the entire article). Species assignment is one of the bottlenecks of gene name disambiguation. In our evaluation, recognizing the focus species of a gene mention in four different scopes improved the gene name disambiguation. We used the species cue words extracted from articles to estimate the relevance between an article and a species. The relevance score was calculated by our proposed entities frequency-augmented invert species frequency (EF-AISF) formula, which represents the importance of an entity to a species. We also defined a relation guide factor (RGF) to normalize the relevance score. Our method not only achieved better performance than previous methods but also can handle the articles that do not specifically mention a species. In the DECA corpus, we outperformed previous studies and obtained an accuracy of 88.22 percent.
机译：物种检测是文本挖掘领域中的重要主题。根据研究主题的重要性（例如，将物种分配给基因和记录重点物种检测），一些研究专门针对单个主题。但是，迄今为止，没有研究者将物种检测作为一个普遍问题进行讨论。因此，我们开发了一种多范围的物种检测模型，以识别不同范围（即整个文章的基因提及，句子，段落和全局范围）的重点物种。物种分配是消除基因名称歧义的瓶颈之一。在我们的评估中，认识到四个不同范围内提到的基因的重点物种改善了基因名称的歧义消除。我们使用从文章中提取的物种提示词来估计文章和物种之间的相关性。相关分数是通过我们提出的实体频率增强的反转物种频率（EF-AISF）公式计算的，该公式表示实体对物种的重要性。我们还定义了一个关系指导因子（RGF）来标准化相关性得分。我们的方法不仅比以前的方法具有更好的性能，而且可以处理未特别提及物种的文章。在DECA语料库中，我们优于以前的研究，并获得了88.22％的准确性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号