Automatic Extraction of Ranked SNP-Phenotype Associations from Literature through Detecting Neural Candidates, Negation and Modality Markers

• URL: http://arxiv.org/abs/2012.00902v1
• Date: Wed, 2 Dec 2020 00:03:07 GMT
• Title: Automatic Extraction of Ranked SNP-Phenotype Associations from Literature through Detecting Neural Candidates, Negation and Modality Markers
• Authors: Behrouz Bokharaeian, Alberto Diaz
• Abstract summary: There is no available method for extracting the association of SNP-phenotype from text. The experiments show that negation cues and scope as well as detecting neutral candidates can be employed for implementing a superior relation extraction method. A modality based approach is proposed to estimate the confidence level of the extracted association.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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