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Featuring:

Amirreza Daghighi: “Integrated Chemical Environment: A comparative study of machine learning models to predict acute oral toxicity”

ASCCT 14th Annual Meeting Travel Award Recipient

Ivo Djidrovski, PhD: “Augmenting Chemical Hazard and Risk Assessment through Large Language Models and AI Agents”

ASCCT 14th Annual Meeting Ray Tice Tox21 Student Award

Nyssa Tucker: “RADISH: ROBOKOP Assisted Discovery of in silico Hypotheses - A case study of mechanisms linking nuclear receptors to liver disease”

ASCCT 14th Annual Meeting Travel Award Recipient

A brief Q&A session will follow each presentation.

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ABSTRACTS

ABSTRACT COMING SOON: Integrated Chemical Environment: A comparative study of machine learning models to predict acute oral toxicity

ABSTRACT COMING SOON: Augmenting Chemical Hazard and Risk Assessment through Large Language Models and AI Agents

RADISH: ROBOKOP Assisted Discovery of in silico Hypotheses - A case study of mechanisms linking nuclear receptors to liver disease
Adverse Outcome Pathways (AOPs) describe the cascade of biological events leading to toxic outcomes. An AOP is built through manual efforts of subject matter experts identifying empirical molecular or biological evidence for each node in the chain. Recent developments in biomedical information retrieval led to the emergence of biomedical knowledge graphs (KG), which integrate multiple elementary semantic triples, i.e., special ‘subject-predicate-object’ relationships between chemical and biological entities (such as ‘chemical causes hepatotoxicity’) described in biomedical literature or databases. One KG, ROBOKOP, is a biomedical publicly available graph integrating 50+ databases to include over 9M nodes and 140M edges.
ROBOKOP Assisted Discovery of in silico Hypotheses (RADisH) is a methodology for automated discovery of AOPs based on graph mining algorithms. We evaluate RADISH using expert-defined AOPs for fatty liver disease. Of the approximately 50 unique events described across the thirteen AOPs in AOPwiki, KG queries considering just one nuclear receptor, AhR, resulted in 34 genes of which 26 were partial or complete matches to AOP events implying a recall of ~70%. The remaining 8 genes not matched to existing AOP events were evaluated for plausibility within the context of fatty liver disease and investigated for potential applicability as hypothetical mechanistic event candidates. The use of these novel hypothetical mechanisms can support future development of quantitative models predicting chemical toxicity. Furthermore, these hypothetical mechanisms can be fed back to biomedical researchers to evaluate the plausibility of these connections and broaden the scope of biomedical knowledge.

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ABOUT THE PRESENTERS

Amirreza Daghighi - Bio Coming Soon

Ivo Djidrovski, PhD - Bio Coming Soon

Nyssa Tucker is a 5th year Computational Toxicology Ph.D. Candidate at UNC Chapel Hill. Along with the research interests embedded in the associated abstract, Nyssa is also passionate about building strong networks between people towards a resilient community-focused system of collaboration addressing pressing social concerns. Nyssa is a member of SOT, UE150, and UNC's Molecular Modeling Laboratory.

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The recording and select materials from this webinar will be posted on the ASCCT webinar archive: https://ASCCTox.org/Webinar-Archive

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