Evaluating GPT and BERT models for protein-protein interaction identification in biomedical text.

ABSTRACT:

Motivation

Detecting protein-protein interactions (PPIs) is crucial for understanding genetic mechanisms, disease pathogenesis, and drug design. As biomedical literature continues to grow rapidly, there is an increasing need for automated and accurate extraction of these interactions to facilitate scientific discovery. Pretrained language models, such as generative pretrained transformers and bidirectional encoder representations from transformers, have shown promising results in natural language processing tasks.

Results

We evaluated the performance of PPI identification using multiple transformer-based models across three manually curated gold-standard corpora: Learning Language in Logic with 164 interactions in 77 sentences, Human Protein Reference Database with 163 inter

SUBMITTER: Rehana H 

PROVIDER: S-EPMC11419952 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature