ArticleCurrent genomics2025
AgriBioNER: A Named Entity Recognition Tool for Identification of ncRNA and Diseases in Agricultural Literature.
Article in Current genomics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: The rapid growth of agricultural and genomic research has generated a vast body of literature, making manual extraction of non-coding RNAs (ncRNAs) and agricultural disease entities increasingly challenging. Although pretrained transformer models such as BioBERT-v1.1, PubMedBERT-base-uncased-abstract-fulltext, DeBERTa-v3-large, and RoBERTa-base perform well in biomedical NLP, they lack task-specific Named Entity Recognition (NER) layers adapted to agricultural terminology. This study presents AgriBioNER, a domain-adapted NER framework for accurately extracting ncRNA and disease entities from agricultural literature. Methods: A curated dataset of agricultural abstracts related to ncRNAs and diseases was retrieved from PubMed. Four transformer models were fine-tuned using the spaCy transformer pipeline, and a classical spaCy model (en_core_web_lg) was trained separately. Performance was evaluated using precision, recall, F1-score, statistical significance testing, 500-iteration bootstrap validation, error analysis, and assessment of prediction time and scalability. Results: Transformer models showed consistent performance, while en_core_web_lg exhibited higher asymmetric errors. PubMedBERT-base-uncased-abstract-fulltext achieved the best results with a precision of 0.8351 ± 0.0139, recall of 0.8732 ± 0.0117, and F1-score of 0.8537 ± 0.0111. It processed abstracts in approximately 3.56 seconds per abstract, supporting large-scale deployment. Discussion: Domain-adapted transformer models substantially improved ncRNA-disease entity recognition in agricultural texts. Enhanced contextual representations enabled a better understanding of domain-specific terminology. These improvements demonstrate the effectiveness of specialized fine-tuning for agricultural NLP tasks. Conclusion: AgriBioNER offers a reliable framework for automated extraction of ncRNA and disease entities. The system reduces manual curation efforts while maintaining high accuracy and computational efficiency. It supports data-driven advancements in agricultural genomics and biotechnology research.
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