Integrative Data Mining Approach: Case Study with Adverse Outcome Pathway Network Leading to Pulmonary Fibrosis

An adverse outcome pathway (AOP) framework can be applied
as an
efficient tool for the rapid screening of environmental chemicals.
For the development of an AOP, a database mining approach can support
an expert derivation approach by gathering a wider range of evidence
than in a literature review. In this study, data from various databases
were integrated and analyzed to supplement the AOP leading to pulmonary
fibrosis by analyzing additional evidence using a data mining approach
and establishing an application domain for chemicals. First, we collected
chemicals, genes, and phenotypes that were studied and related to
pulmonary fibrosis through the Comparative Toxicogenomics Database
(CTD). CGPD-tetramers constructed by linking each related chemical,
gene, phenotype, and disease can provide the basic components for
the assembly of putative AOPs. Next, an AOP network was established
by connecting eight existing AOPs for pulmonary fibrosis developed
by expert derivation from the AOP Wiki. Finally, the pulmonary fibrosis
AOP network was proposed by integrating the AOP network from AOP Wiki
and the CGPD-tetramers from the CTD. To prioritize potential chemical
stressors in the AOP network, 61 chemicals were ranked using the relevance
of the chemical to the AOP and chemical exposure information from
the CompTox Chemicals Dashboard. The approach proposed in this study
can guide the utilization of available evidence from various databases
as well as the literature in constructing AOP networks related to
specific diseases.