# BioBERT Based SNP-traits Associations Extraction from Biomedical Literature

arXiv: https://arxiv.org/abs/2308.02569 · HTML: https://mlsanity.com/paper/2308.02569/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 41 references that are in the map.
- First posted on arXiv 3 Aug 2023.

## Builds on (most cited first, 3 of 3)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Chemical-protein relation extraction with ensembles of SVM, CNN, and RNN models](https://mlsanity.com/paper/1802.01255/) (2018)

## Built on by (most cited first, 1 of 1)

- [Dental Severity Assessment through Few-shot Learning and SBERT Fine-tuning](https://mlsanity.com/paper/2402.15755/) (2024)

## Abstract (arXiv)

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.