Improving large language models for miRNA information extraction via prompt engineering

Large language models (LLMs) demonstrate significant potential in biomedical knowledge discovery, yet their performance in extracting fine-grained biological information, such as miRNA, remains insufficiently explored. Accurate extraction of miRNA-related information is essential for understanding disease mechanisms and identifying biomarkers. This study aims to comprehensively evaluate the capabilities of LLMs in miRNA information extraction through diverse prompt learning strategies. Three high-quality miRNA information extraction datasets were constructed to support the benchmarking and training of generative LLMs, specifically Re-Tex, Re-miR and miR-Cancer. These datasets encompass three types of entities: miRNAs, genes, and diseases, along with their relationships. The accuracy and reliability of three LLMs, including GPT-4o, Gemini, and Claude, were evaluated and compared with traditional models. Different prompt engineering strategies were implemented to enhance the LLMs' performance, including baseline prompts, 5-shot Chain of Thought prompts, and generated knowledge prompts. The combination of optimized prompt strategies significantly improved overall entity extraction performance across both trained and untrained datasets. Generated knowledge prompting achieved the highest performance, with maximum F1 scores of 76.6 % for entity extraction and 54.8 % for relationship extraction. Comparative analysis indicated GPT-4o exhibited superior performance to Gemini, while Claude showed the lowest performance levels. Extraction accuracy varied considerably across entity types, with miRNA recognition achieving the highest performance and gene/protein identification demonstrating the lowest accuracy levels. Furthermore, binary relationship extraction accuracy was significantly lower than entity extraction performance. The three evaluated LLMs showed similarly limited capability in relationship extraction tasks, with no statistically significant differences observed between models. Finally, comparison with conventional computational methods revealed LLMs have not yet exceeded traditional methods in this specialized domain. This study established high-quality miRNA datasets to support information extraction and knowledge discovery. The overall performance of LLMs in this study proved limited, and challenges remain in processing miRNA-related information extraction. However, optimized prompt combinations can substantially improve performance. Future work should focus on further refinement of LLMs to accelerate the discovery and application of potential diagnostic and therapeutic targets.