  Since the launch of various generative AI tools, scientists have been
striving to evaluate their capabilities and contents, in the hope of
establishing trust in their generative abilities. Regulations and guidelines
are emerging to verify generated contents and identify novel uses. we aspire to
demonstrate how ChatGPT claims are checked computationally using the rigor of
network models. We aim to achieve fact-checking of the knowledge embedded in
biological graphs that were contrived from ChatGPT contents at the aggregate
level. We adopted a biological networks approach that enables the systematic
interrogation of ChatGPT's linked entities. We designed an ontology-driven
fact-checking algorithm that compares biological graphs constructed from
approximately 200,000 PubMed abstracts with counterparts constructed from a
dataset generated using the ChatGPT-3.5 Turbo model. In 10-samples of 250
randomly selected records a ChatGPT dataset of 1000 "simulated" articles , the
fact-checking link accuracy ranged from 70% to 86%. This study demonstrated
high accuracy of aggregate disease-gene links relationships found in
ChatGPT-generated texts.
