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A large comparison of integrated SAR/QSAR models of the Ames test for mutagenicity($)
Istituto di Ricerche Farmacologiche Mario Negri (IRCCS), Milano, Italy.
Istituto di Ricerche Farmacologiche Mario Negri (IRCCS), Milano, Italy.
Istituto di Ricerche Farmacologiche Mario Negri (IRCCS), Milano, Italy.
Istituto di Ricerche Farmacologiche Mario Negri (IRCCS), Milano, Italy.
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2018 (English)In: SAR and QSAR in environmental research (Print), ISSN 1062-936X, E-ISSN 1029-046X, Vol. 29, no 8, p. 591-611Article in journal (Refereed) Published
Abstract [en]

Results from the Ames test are the first outcome considered to assess the possible mutagenicity of substances. Many QSAR models and structural alerts are available to predict this endpoint. From a regulatory point of view, the recommendation from international authorities is to consider the predictions of more than one model and to combine results in order to develop conclusions about the mutagenicity risk posed by chemicals. However, the results of those models are often conflicting, and the existing inconsistency in the predictions requires intelligent strategies to integrate them. In our study, we evaluated different strategies for combining results of models for Ames mutagenicity, starting from a set of 10 diverse individual models, each built on a dataset of around 6000 compounds. The novelty of our study is that we collected a much larger set of about 18,000 compounds and used the new data to build a family of integrated models. These integrations used probabilistic approaches, decision theory, machine learning, and voting strategies in the integration scheme. Results are discussed considering balanced or conservative perspectives, regarding the possible uses for different purposes, including screening of large collection of substances for prioritization.

Place, publisher, year, edition, pages
Taylor & Francis, 2018. Vol. 29, no 8, p. 591-611
Keywords [en]
prediction of mutagenicity, Ames test, ensembles of models, integrating SAR and QSAR, naive Bayes, Dempster-Shafer theory, self-organizing neural networks, GMDH
National Category
Computer and Information Sciences Earth and Related Environmental Sciences Pharmacology and Toxicology
Identifiers
URN: urn:nbn:se:oru:diva-83038DOI: 10.1080/1062936X.2018.1497702ISI: 000442692500003PubMedID: 30052064Scopus ID: 2-s2.0-85050952588OAI: oai:DiVA.org:oru-83038DiVA, id: diva2:1439300
Funder
Swedish Research Council, 2016-02031Knut and Alice Wallenberg Foundation, 2013.0253
Note

Ytterligare forskningsfinansiärer:

EU LIFE VERMEER projec, Grant Number: LIFE16 ENV/IT/000167

UBA, Germany [Projects JANUS]

Available from: 2018-09-18 Created: 2020-06-12 Last updated: 2020-07-16Bibliographically approved

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Norinder, Ulf

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