Automatic Extraction of SNP-Trait Associations from Text through Detecting Linguistic-Based Negation (original) (raw)
— Genome-wide association (GWA) studies form an important category of research studies in personalized medicine which discuss on associations between single-nucleotide polymorphisms (SNPs) and phenotypic traits. Considering the fast growing rate of GWA studies, automatic extraction of SNP-Traits associations from text is a highly demanding task. In this research, first an SNP-Trait association corpus is produced and then a non-supervised relation extraction method grounded on linguistic-based negation detection method is proposed. The experiments show that negation cues and scope can be employed as a superior relation extraction method due to uniform polarity of the sentences, small number of neutral examples and concessive clauses in the corpus. The proposed method is a non-supervised relation extraction method which works at the sentence-level with no need to label training data. Moreover, the experiments indicate that the proposed method has a superior performance over the studied sequence kernel method.
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