ArticleNAR genomics and bioinformatics2021
RENET2: high-performance full-text gene-disease relation extraction with iterative training data expansion.
Article in NAR genomics and bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Relation extraction for diet, non-communicable disease and biomarker associations (RECoDe): a CoDiet study.Bioinformatics (Oxford, England) · 2026Article
- From narrative evidence to computable knowledge: a decision-relevant corpus for medicinal herb-disease relationships.Journal of biomedical semantics · 2026Article
- BioTriplex: a full-text annotated corpus for fine-tuning language models in gene-disease relation extraction tasks.Bioinformatics (Oxford, England) · 2026Article
- A FAIR gold-standard benchmark for geographic entity recognition in cancer genomics literature for biomedical NLP.Bioinformatics advances · 2026Article
- An NLP-based method to mine gene and function relationships from published articles.Scientific reports · 2025Article
- LSD600: the first corpus of biomedical abstracts annotated with lifestyle-disease relations.Database : the journal of biological databases and curation · 2025Article
- Hybrid deep learning models for text-based identification of gene-disease associations.BioImpacts : BI · 2025Article
- RegulaTome: a corpus of typed, directed, and signed relations between biomedical entities in the scientific literature.Database : the journal of biological databases and curation · 2024Article
- DUVEL: an active-learning annotated biomedical corpus for the recognition of oligogenic combinations.Database : the journal of biological databases and curation · 2024Article
- Surveying biomedical relation extraction: a critical examination of current datasets and the proposal of a new resource.Briefings in bioinformatics · 2024Review
- Exploring COVID-related relationship extraction: Contrasting data sources and analyzing misinformation.Heliyon · 2024Article
- SCREENER: Streamlined collaborative learning of NER and RE model for discovering gene-disease relations.PloS one · 2023Article
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Authors and funding
5 authors.
Funding
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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 72.13% for extracting gene-disease associations from an annotated full-text dataset, which was 27.22, 30.30, 29.24 and 23.87% higher than BeFree, DTMiner, BioBERT and RENET, respectively. We applied RENET2 to (i) ∼1.89M full-text articles from PubMed Central and found ∼3.72M gene-disease associations; and (ii) the LitCovid articles and ranked the top 15 proteins associated with COVID-19, supported by recent articles. RENET2 is an efficient and accurate method for full-text gene-disease association extraction. The source-code, manually curated abstract/full-text training data, and results of RENET2 are available at GitHub.
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Registered trials
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