{
  "abstract": "The quantification of adverse outcome pathways (qAOPs) involving multiple converging pathways remains a fundamental challenge in toxicology. This difficulty is worsened by small in vitro datasets with missing values and heteroscedasticity, which limit the estimation of non-linear response functions and the robust estimation of uncertainty. To overcome these limitations, we developed a framework for estimating a Bayesian qAOP Ensemble (Bayesian qAOPE) that combines heteroscedastic dose-response functions with continuous splines and linear models. By combining multiple converging non-linear pathways through Bayesian stacking, the most meaningful pathways are identified, and strongly correlated responses are naturally accounted for. Applying our approach to a hepatic steatosis dataset, we demonstrate by exact Bayesian leave-one-out cross-validation that our framework yields an exceptional predictive performance suitable for the derivation of a probabilistically weighted Benchmark Dose (BMD). Our framework is highly adaptable and provides a chemical-agnostic, mathematically founded blueprint for integrating multiple converging pathways in a Bayesian network.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Pesticides Safety, German Federal Institute for Risk Assessment (BfR), Max- Dohrn-Str. 8-10, 10589 Berlin, Germany"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "A. M. Steinbach"
    },
    {
      "name": "P. Marx-Stoelting"
    },
    {
      "affs": [
        0
      ],
      "name": "C. T. Willenbockel"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5ce1pr745w8k5.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.20.760409",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.20.760409v1.full",
  "id": "5ce1pr745w8k5",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.20.760409v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "pharmacology and toxicology"
    ],
    [
      "openalex",
      "2613|Statistical Methods and Bayesian Inference"
    ]
  ],
  "openalex": "W7223678931",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.20.760409v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-20",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/5ce1pr745w8k5.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:38Z",
      "native_id": "10.64898/2026.10.20.760409",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.20.760409v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "biorxiv:pharmacology and toxicology",
      "subfield": "pharmacology-toxicology"
    }
  ],
  "title": "Bayesian Stacking of Quantitative Adverse Outcome Pathways Isolates Primary Drivers of Liver Steatosis",
  "title_text": "Bayesian Stacking of Quantitative Adverse Outcome Pathways Isolates Primary Drivers of Liver Steatosis",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:38Z",
  "url": "https://research.draconitic.com/paper/5ce1pr745w8k5",
  "venue": "bioRxiv",
  "versions": []
}