  Detecting protein-protein interactions (PPIs) is crucial for understanding
genetic mechanisms, disease pathogenesis, and drug design. However, with the
fast-paced growth of biomedical literature, there is a growing need for
automated and accurate extraction of PPIs to facilitate scientific knowledge
discovery. Pre-trained language models, such as generative pre-trained
transformers (GPT) and bidirectional encoder representations from transformers
(BERT), have shown promising results in natural language processing (NLP)
tasks. We evaluated the performance of PPI identification of multiple GPT and
BERT models using three manually curated gold-standard corpora: Learning
Language in Logic (LLL) with 164 PPIs in 77 sentences, Human Protein Reference
Database with 163 PPIs in 145 sentences, and Interaction Extraction Performance
Assessment with 335 PPIs in 486 sentences. BERT-based models achieved the best
overall performance, with BioBERT achieving the highest recall (91.95%) and
F1-score (86.84%) and PubMedBERT achieving the highest precision (85.25%).
Interestingly, despite not being explicitly trained for biomedical texts, GPT-4
achieved commendable performance, comparable to the top-performing BERT models.
It achieved a precision of 88.37%, a recall of 85.14%, and an F1-score of
86.49% on the LLL dataset. These results suggest that GPT models can
effectively detect PPIs from text data, offering promising avenues for
application in biomedical literature mining. Further research could explore how
these models might be fine-tuned for even more specialized tasks within the
biomedical domain.
