LLM Based Biological Named Entity Recognition from Scientific Literature
Abstract
Recently, the application of Large Language Models (LLMs) in the field of natural language processing has witnessed remarkable growth, revolutionizing the field of bioinformatics by automating the extraction of biological entities from scientific literature. This study presents the development and evaluation of a Biological Named Entity Recognizer (BNER) using a pre-trained Large Language Model (LLM) refined through prompt engineering. The BNER was tailored to identify proteins, genes, and small molecules within scientific texts, specifically targeting the context of p53 protein-related research. To assess the BNER's efficacy, we curated a dataset comprising ten paragraphs extracted from the abstracts and significant sections of five high-relevance scientific papers. The system's performance was quantified through an entity recognition task, resulting in 51 true positives (TP), 10 false positives (FP), and 3 false negatives (FN). The BNER achieved an F1 score of 0.887, demonstrating a high degree of precision and recall. These results validate the utility of LLMs in bioinformatics and highlight the BNER's potential to support and accelerate scientific discovery by providing accurate, structured data outputs suitable for comprehensive analysis.
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LLMs have already shown competency in pilot studies of automated data extraction in biology, materials science, and pharmacology.
“Notably, LLMs have already shown competency in pilot studies of automated data extraction in biology [32], materials science [33], and pharmacology [33]”
From Large Language Model Applications for Health Information Extraction in Oncology: Scoping Review (2025) · cites this paper as [32]
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- Authors
- Jung, S. J., Kim, H., & Jang, K. S.
- Affiliations
- En-Core Inc.,Division of DG and AI Business,Seoul,South Korea
- En-Core Inc.,Division of DT Business,Seoul,South Korea
- ORCID
- Kim, Hajung
- Venue
- 2024 IEEE International Conference on Big Data and Smart Computing (BigComp)
- Publisher
- IEEE
- Published
- 2024-02-18
- Pages
- 433-435
- OpenAlex
- W4394713233
- Cited by
- 19
- References
- 8
- Citations by year
- 2026: 8 · 2025: 10 · 2024: 1
- FWCI
- 34.34
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Big Data and Smart Computing (BigComp) (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Text Analysis Techniques
- Keywords
- large language models, prompt engineering, p53, protein extraction, gene extraction, scientific publications, bioinformatics
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