Abstract PubTator 3.0 (https://www. ncbi. nlm. nih. gov/research/pubtator3/) is a biomedical
literature resource using state-of-the-art AI techniques to offer semantic and relation …

Abstract The BioRED track at BioCreative VIII calls for a community effort to identify,
semantically categorize, and highlight the novelty factor of the relationships between …

Objective: Relation extraction is an essential task in the field of biomedical literature mining
and offers significant benefits for various downstream applications, including database …

The automatic recognition of biomedical relationships is an important step in the semantic
understanding of the information contained in the unstructured text of the published …

Expert curation is essential to capture knowledge of enzyme functions from the scientific
literature in FAIR open knowledgebases but cannot keep pace with the rate of new …

Biomedical named entity recognition (BioNER) aims to identify and classify biomedical
entities (ie, diseases, chemicals, and genes) from text into predefined classes. This process …

Biomedical relation extraction from scientific publications is a key task in biomedical natural
language processing (NLP) and can facilitate the creation of large knowledge bases, enable …

Dynamic changes in protein glycosylation impact human health and disease progression.
However, current resources that capture disease and phenotype information focus primarily …

With the exponential growth of biomedical literature, leveraging Large Language Models
(LLMs) for automated medical knowledge understanding has become increasingly critical …

Information extraction techniques, including named entity recognition (NER) and relation
extraction (RE), are crucial in many domains to support making sense of vast amounts of …