BioBERT Based SNP-traits Associations Extraction from Biomedical Literature

TL;DR

This paper introduces a BioBERT-GRU model for extracting SNP-traits associations from biomedical literature, demonstrating superior performance over previous methods with high precision, recall, and F1-score.

Contribution

The paper presents a novel BioBERT-GRU approach specifically designed for SNP-traits association extraction, outperforming existing machine learning and deep learning techniques.

Findings

BioBERT-GRU achieved 0.883 precision

It attained 0.882 recall

F1-score was 0.881

Abstract

Scientific literature contains a considerable amount of information that provides an excellent opportunity for developing text mining methods to extract biomedical relationships. An important type of information is the relationship between singular nucleotide polymorphisms (SNP) and traits. In this paper, we present a BioBERT-GRU method to identify SNP- traits associations. Based on the evaluation of our method on the SNPPhenA dataset, it is concluded that this new method performs better than previous machine learning and deep learning based methods. BioBERT-GRU achieved the result a precision of 0.883, recall of 0.882 and F1-score of 0.881.