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Applying knowledge-driven mechanistic inference to toxicogenomics

By Ignacio J. Tripodi, Tiffany J Callahan, Jessica T. Westfall, Nayland S. Meitzer, Robin D Dowell, Lawrence E Hunter

Posted 25 Sep 2019
bioRxiv DOI: 10.1101/782011 (published DOI: 10.1016/j.tiv.2020.104877)

Government regulators and others concerned about toxic chemicals in the environment hold that a mechanistic, causal explanation of toxicity is strongly preferred over a statistical or machine learning-based prediction by itself. Elucidating a mechanism of toxicity is, however, a costly and time-consuming process that requires the participation of specialists from a variety of fields, often relying on animal models. We present an innovative mechanistic inference framework (MechSpy), which can be used as a hypothesis generation aid to narrow the scope of mechanistic toxicology analysis. MechSpy generates hypotheses of the most likely mechanisms of toxicity, by combining a semantically-interconnected knowledge representation of human biology, toxicology and biochemistry with gene expression time series on human tissue. Using vector representations of biological entities, MechSpy seeks enrichment in a manually-curated list of high-level mechanisms of toxicity, represented as biochemically- and causally-linked ontology concepts. Besides predicting the canonical mechanism of toxicity for many well-studied compounds, we experimentally validated some of our predictions for other chemicals without an established mechanism of toxicity. This framework can be modified to include additional mechanisms of toxicity, and is generalizable to other types of mechanisms of human biology.

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