ArticleBMC bioinformatics2023
Leveraging pre-trained language models for mining microbiome-disease relationships.
Article in BMC bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applications and Concerns of ChatGPT and Other Conversational Large Language Models in Health Care: Systematic Review.Journal of medical Internet research · 2024Pooled it
- The DSMZ Digital Diversity Annotation Hub: a pipeline for database expansion via text mining and human curation.Journal of integrative bioinformatics · 2026Article
- Applications and prospects of artificial intelligence and digital medicine in pediatric nasal skull base tumors.Pediatric investigation · 2026Review
- Review
- Creating an engine of scientific discovery, an Indo-Japan perspective: learnings from IJNTC 2025 workshop.NPJ systems biology and applications · 2026Review
- Review
- Evaluating the quality and readability of AI-generated information on adenomyosis: a comparative analysis of ChatGPT and deepseek regarding query-model consistency.Frontiers in public health · 2026Article
- Integrating natural language processing and genome analysis enables accurate bacterial phenotype prediction.NAR genomics and bioinformatics · 2025Article
- MINERVA-microbiome network research and visualization atlas: a scalable knowledge graph for mapping microbiome-disease associations.Briefings in bioinformatics · 2025Article
- DiMB-RE: mining the scientific literature for diet-microbiome associations.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- LLM-IE: a python package for biomedical generative information extraction with large language models.JAMIA open · 2025Article
- Leveraging large language models for knowledge-free weak supervision in clinical natural language processing.Scientific reports · 2025Article
- Retrieval Augmented Therapy Suggestion for Molecular Tumor Boards: Algorithmic Development and Validation Study.Journal of medical Internet research · 2025Article
- Comparison of NLP machine learning models with human physicians for ASA Physical Status classification.NPJ digital medicine · 2024Article
- Large language model to multimodal large language model: A journey to shape the biological macromolecules to biological sciences and medicine.Molecular therapy. Nucleic acids · 2024Review
- Generative artificial intelligence performs rudimentary structural biology modeling.Scientific reports · 2024Article
- Quantifying the massive pleiotropy of microRNA: a human microRNA-disease causal association database generated with ChatGPT.bioRxiv : the preprint server for biology · 2024Article
- Leveraging Large Language Models for Knowledge-free Weak Supervision in Clinical Natural Language Processing.Research square · 2024Article
- Generative artificial intelligence performs rudimentary structural biology modeling.bioRxiv : the preprint server for biology · 2024Article
- A Systematic Review of ChatGPT and Other Conversational Large Language Models in Healthcare.medRxiv : the preprint server for health sciences · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe growing recognition of the microbiome's impact on human health and well-being has prompted extensive research into discovering the links between microbiome dysbiosis and disease (healthy) states. However, this valuable information is scattered in unstructured form within biomedical literature. The structured extraction and qualification of microbe-disease interactions are important. In parallel, recent advancements in deep-learning-based natural language processing algorithms have revolutionized language-related tasks such as ours. This study aims to leverage state-of-the-art deep-learning language models to extract microbe-disease relationships from biomedical literature.
resultsIn this study, we first evaluate multiple pre-trained large language models within a zero-shot or few-shot learning context. In this setting, the models performed poorly out of the box, emphasizing the need for domain-specific fine-tuning of these language models. Subsequently, we fine-tune multiple language models (specifically, GPT-3, BioGPT, BioMedLM, BERT, BioMegatron, PubMedBERT, BioClinicalBERT, and BioLinkBERT) using labeled training data and evaluate their performance. Our experimental results demonstrate the state-of-the-art performance of these fine-tuned models ( specifically GPT-3, BioMedLM, and BioLinkBERT), achieving an average F1 score, precision, and recall of over [Formula: see text] compared to the previous best of 0.74.
conclusionOverall, this study establishes that pre-trained language models excel as transfer learners when fine-tuned with domain and problem-specific data, enabling them to achieve state-of-the-art results even with limited training data for extracting microbiome-disease interactions from scientific publications.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.