Leveraging pre-trained language models for mining microbiome-disease relationships.

ABSTRACT:

Background

The 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.

Results

In this study, we first evaluate multiple pre-trained large language models with

SUBMITTER: Karkera N 

PROVIDER: S-EPMC10357883 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature