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

TL;DR

This paper introduces a novel linguistic-based relation extraction method for identifying SNP-phenotype associations from literature, incorporating negation and modality detection to assess confidence levels, advancing personalized medicine research.

Contribution

It presents a new relation extraction approach that uses negation and modality detection to improve accuracy and confidence estimation in SNP-phenotype association extraction from text.

Findings

Negation cues and scope improve extraction accuracy.

The modality-based confidence estimation outperforms existing methods.

The approach is effective for biomedical relation extraction tasks.

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.