RENET2: high-performance full-text gene-disease relation extraction with iterative training data expansion.

ABSTRACT: Relation extraction (RE) is a fundamental task for extracting gene-disease associations from biomedical text. Many state-of-the-art tools have limited capacity, as they can extract gene-disease associations only from single sentences or abstract texts. A few studies have explored extracting gene-disease associations from full-text articles, but there exists a large room for improvements. In this work, we propose RENET2, a deep learning-based RE method, which implements Section Filtering and ambiguous relations modeling to extract gene-disease associations from full-text articles. We designed a novel iterative training data expansion strategy to build an annotated full-text dataset to resolve the scarcity of labels on full-text articles. In our experiments, RENET2 achieved an F1-score of 72

SUBMITTER: Su J 

PROVIDER: S-EPMC8256824 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature