BioBERT Based SNP-traits Associations Extraction from Biomedical
  Literature
Mohammad Dehghani Behrouz Bokharaeian Zahra Yazdanparast
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.