BioBERT Based SNP-traits Associations Extraction from Biomedical Literature
This work addresses the need for automated extraction of SNP-traits associations from scientific literature, which is incremental as it builds on existing text mining and deep learning approaches.
The paper tackled the problem of extracting relationships between singular nucleotide polymorphisms (SNP) and traits from biomedical literature, achieving a precision of 0.883, recall of 0.882, and F1-score of 0.881 with their BioBERT-GRU method.
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.