Automatic extraction of ranked SNP-phenotype associations from text using a BERT-LSTM-based method.

ABSTRACT: Extraction of associations of singular nucleotide polymorphism (SNP) and phenotypes from biomedical literature is a vital task in BioNLP. Recently, some methods have been developed to extract mutation-diseases affiliations. However, no accessible method of extracting associations of SNP-phenotype from content considers their degree of certainty. In this paper, several machine learning methods were developed to extract ranked SNP-phenotype associations from biomedical abstracts and then were compared to each other. In addition, shallow machine learning methods, including random forest, logistic regression, and decision tree and two kernel-based methods like subtree and local context, a rule-based and a deep CNN-LSTM-based and two BERT-based methods were developed in this study to extract as

SUBMITTER: Bokharaeian B 

PROVIDER: S-EPMC10099837 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature