论文标题
通过检测神经候选，否定和方式标记的文献自动提取排名SNP - 表型关联
Automatic Extraction of Ranked SNP-Phenotype Associations from Literature through Detecting Neural Candidates, Negation and Modality Markers
论文作者
论文摘要
全基因组关联（GWA）构成了有关个性化医学和药物基因组学的重要研究。最近，很少开发出提取突变脉络关联的方法。但是，没有可用的方法可以从认为对关联的信心中提取SNP - 表型的关联。在这项研究中，首先提出了一种关系提取方法，该方法依赖于基于语言的否定检测和中性候选者。实验表明，否定线索和范围以及检测中性候选者可以用于实施一种出色的关系提取方法，该方法的表现优于基于内核的句子，因为句子的先天性均匀的句子和小体中的少量复杂句子。此外，提出了一种基于模态的方法来估计提取关联的置信度，该方法可用于评估报告关联的可靠性。关键词：SNP，表型，生物医学关系提取，否定检测。
Genome-wide association (GWA) constitutes a prominent portion of studies which have been conducted on personalized medicine and pharmacogenomics. Recently, very few methods have been developed for extracting mutation-diseases associations. However, there is no available method for extracting the association of SNP-phenotype from text which considers degree of confidence in associations. In this study, first a relation extraction method relying on linguistic-based negation detection and neutral candidates is proposed. The experiments show that negation cues and scope as well as detecting neutral candidates can be employed for implementing a superior relation extraction method which outperforms the kernel-based counterparts due to a uniform innate polarity of sentences and small number of complex sentences in the corpus. Moreover, a modality based approach is proposed to estimate the confidence level of the extracted association which can be used to assess the reliability of the reported association. Keywords: SNP, Phenotype, Biomedical Relation Extraction, Negation Detection.
