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Artificial Intelligence in the Life Sciences

Volume 5, June 2024, 100095
Artificial Intelligence in the Life Sciences

Research Article
Rationalism in the face of GPT hypes: Benchmarking the output of
large language models against human expert-curated biomedical
knowledge graphs

Author links open overlay panelNegin Sadat Babaiha ^a ^b, Sathvik
Guru Rao ^a, Jurgen Klein ^a, Bruce Schultz ^a, Marc Jacobs ^a, 
Martin Hofmann-Apitius ^a ^b
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Abstract

Biomedical knowledge graphs (KGs) hold valuable information regarding
biomedical entities such as genes, diseases, biological processes,
and drugs. KGs have been successfully employed in challenging
biomedical areas such as the identification of pathophysiology
mechanisms or drug repurposing. The creation of high-quality KGs
typically requires labor-intensive multi-database integration or
substantial human expert curation, both of which take time and
contribute to the workload of data processing and annotation.
Therefore, the use of automatic systems for KG building and
maintenance is a prerequisite for the wide uptake and utilization of
KGs. Technologies supporting the automated generation and updating of
KGs typically make use of Natural Language Processing (NLP), which is
optimized for extracting implicit triples described in relevant
biomedical text sources. At the core of this challenge is how to
improve the accuracy and coverage of the information extraction
module by utilizing different models and tools. The emergence of
pre-trained large language models (LLMs), such as ChatGPT which has
grown in popularity dramatically, has revolutionized the field of
NLP, making them a potential candidate to be used in text-based graph
creation as well. So far, no previous work has investigated the power
of LLMs on the generation of cause-and-effect networks and KGs
encoded in Biological Expression Language (BEL). In this paper, we
present initial studies towards one-shot BEL relation extraction
using two different versions of the Generative Pre-trained
Transformer (GPT) models and evaluate its performance by comparing
the extracted results to a highly accurate, manually curated BEL KG
curated by domain experts.

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Keywords

Large language models (LLMs)
Natural language processing (NLP)
Biomedical text mining
Biomedical knowledge graphs
Biological expression language (BEL)
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(c) 2024 The Authors. Published by Elsevier B.V.

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