Exploring genetic influences on adverse outcome pathways using heuristic simulation and graph data science
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- @Article{ROMANO:2023:comtox,
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author = "Joseph D. Romano and Liang Mei and Jonathan Senn and
Jason H. Moore and Holly M. Mortensen",
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title = "Exploring genetic influences on adverse outcome
pathways using heuristic simulation and graph data
science",
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journal = "Computational Toxicology",
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volume = "25",
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pages = "100261",
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year = "2023",
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ISSN = "2468-1113",
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DOI = "doi:10.1016/j.comtox.2023.100261",
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URL = "https://www.sciencedirect.com/science/article/pii/S2468111323000026",
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keywords = "genetic algorithms, genetic programming, Adverse
outcome pathway, Liver cancer, Graph data science",
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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 Environmental Protection Agency's
Adverse Outcome Pathway Database (AOP-DB;
aopdb.epa.gov) and the UK Biobank's genetic data
repository. We use the AOP-DB to extract
disease-specific AOPs and build graph neural networks
used in our final analyses. We use the UK Biobank to
retrieve real-world genotype and phenotype data, where
genotypes are based on single nucleotide polymorphism
data extracted from the AOP-DB, and phenotypes are
case/control cohorts for the disease of interest (liver
cancer) corresponding to those adverse outcome
pathways. We also use propensity score matching to
appropriately sample based on important covariates
(demographics, comorbidities, and social deprivation
indices) and to balance the case and control
populations in our machine language training/testing
datasets. Finally, we describe a novel putative risk
factor for LC that depends on genetic variation in both
the aryl-hydrocarbon receptor (AHR) and ATP binding
cassette subfamily B member 11 (ABCB11) genes",
- }
Genetic Programming entries for
Joseph D Romano
Liang Mei
Jonathan Senn
Jason H Moore
Holly M Mortensen
Citations