AgriBioNER: A Named Entity Recognition Tool for Identification of ncRNA and Diseases in Agricultural Literature

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.

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.

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.

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.

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.