Automatic Extraction of Ranked SNP-Phenotype Associations from Literature through Detecting Neural Candidates, Negation and Modality Markers
This work addresses the problem of automatically extracting SNP-phenotype associations with confidence levels from text, which is important for researchers in personalized medicine and pharmacogenomics.
This paper proposes a relation extraction method to automatically extract SNP-phenotype associations from literature, incorporating linguistic-based negation detection and neutral candidate identification. The method also estimates the confidence level of extracted associations using a modality-based approach.
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.