Exploring genetic influences on adverse outcome pathways using heuristic simulation and graph data science.

ABSTRACT: Adverse outcome pathways provide a powerful tool for understanding the biological signaling cascades that lead to disease outcomes following toxicity. The framework outlines downstream responses known as key events, culminating in a clinically significant adverse outcome as a final result of the toxic exposure. Here we use the AOP framework combined with artificial intelligence methods to gain novel insights into genetic mechanisms that underlie toxicity-mediated adverse health outcomes. Specifically, we focus on liver cancer as a case study with diverse underlying mechanisms that are clinically significant. Our approach uses two complementary AI techniques: Generative modeling via automated machine learning and genetic algorithms, and graph machine learning. We used data from the US Envir

SUBMITTER: Romano JD 

PROVIDER: S-EPMC10569310 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature