A beta version of AOPxGeneNet has been released through a collaboration that developed from the OpenTox community.
A major challenge in transcriptomics is moving from long lists of differentially expressed genes to biologically and toxicologically interpretable mechanisms. AOPxGeneNet addresses this problem by connecting gene co-expression networks and molecular perturbation signals with the Adverse Outcome Pathway (AOP) framework.
The approach provides a route from molecular signatures toward Key Events and Adverse Outcomes, while combining information on expression changes, network structure and mechanistic knowledge.
MU OmicsLab contributes to this collaborative research direction together with Barry Hardy, Asmaa A. Abdelwahab and Thomas Mohr. The work fits closely with our broader interest in using network biology, transcriptomics and computational toxicology to transform high-dimensional molecular data into testable mechanistic hypotheses.
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