ArticleDatabase : the journal of biological databases and curation2024
Optimized biomedical entity relation extraction method with data augmentation and classification using GPT-4 and Gemini.
Article in Database : the journal of biological databases and curation, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Data curation in cheminformatics: importance and implementation.Journal of cheminformatics · 2026Review
- Fine-tuned GPT-based foundation models effectively reconstruct bacterial transcriptional regulatory networks from literature.Frontiers in artificial intelligence · 2026Article
- Challenges in bioinformatics education courses organization.Biophysical reviews · 2025Review
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3 authors.
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Abstract
Despite numerous research efforts by teams participating in the BioCreative VIII Track 01 employing various techniques to achieve the high accuracy of biomedical relation tasks, the overall performance in this area still has substantial room for improvement. Large language models bring a new opportunity to improve the performance of existing techniques in natural language processing tasks. This paper presents our improved method for relation extraction, which involves integrating two renowned large language models: Gemini and GPT-4. Our new approach utilizes GPT-4 to generate augmented data for training, followed by an ensemble learning technique to combine the outputs of diverse models to create a more precise prediction. We then employ a method using Gemini responses as input to fine-tune the BioNLP-PubMed-Bert classification model, which leads to improved performance as measured by precision, recall, and F1 scores on the same test dataset used in the challenge evaluation. Database URL: https://biocreative.bioinformatics.udel.edu/tasks/biocreative-viii/track-1/.
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