Protein–Protein Interaction Networks Derived from Classical and Machine Learning-Based Natural Language Processing Tools

The study of protein–protein interactions (PPIs)
provides
insight into various biological mechanisms, including the binding
of antibodies to antigens, enzymes to inhibitors or promoters, and
receptors to ligands. Recent studies of PPIs have led to significant
biological breakthroughs. For example, the study of PPIs involved
in the human:SARS-CoV-2 viral infection mechanism aided in the development
of SARS-CoV-2 vaccines. Though several databases exist for the manual
curation of PPI networks, text mining methods have been routinely
demonstrated as useful alternatives for newly studied or understudied
species, where databases are incomplete. Here, the relationship extraction
performance of several open-source classical text processing, machine
learning (ML)-based natural language processing (NLP), and large language
model (LLM)-based NLP tools was compared. Overall, our results indicated
that networks derived from classical methods tend to have high true
positive rates at the expense of having overconnected networks, ML-based
NLP methods have lower true positive rates but networks with the closest
structures to the target network, and LLM-based NLP methods tend to
exist between the two other approaches, with variable performances.
The selection of a specific NLP approach should be tied to the needs
of a study and text availability, as models varied in performance
due to the amount of text provided.