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    Using Pareto points for model identification in predictive toxicology

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    Publication date
    2013
    Author
    Palczewska, Anna Maria
    Neagu, Daniel
    Ridley, Mick J.
    Keyword
    Predictive toxicology; Model identification; Pareto optimality; Model combination; Tetrahymena-pyriformis; Qsar models; Chemical similarity; Validation; Toxicity; Domain; Qspr; Tool; Set
    
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    Abstract
    Predictive toxicology is concerned with the development of models that are able to predict the toxicity of chemicals. A reliable prediction of toxic effects of chemicals in living systems is highly desirable in cosmetics, drug design or food protection to speed up the process of chemical compound discovery while reducing the need for lab tests. There is an extensive literature associated with the best practice of model generation and data integration but management and automated identification of relevant models from available collections of models is still an open problem. Currently, the decision on which model should be used for a new chemical compound is left to users. This paper intends to initiate the discussion on automated model identification. We present an algorithm, based on Pareto optimality, which mines model collections and identifies a model that offers a reliable prediction for a new chemical compound. The performance of this new approach is verified for two endpoints: IGC50 and LogP. The results show a great potential for automated model identification methods in predictive toxicology.
    URI
    http://hdl.handle.net/10454/9709
    Version
    No full-text in the repository
    Citation
    Palczewska A, Neagu D and Ridley M (2013) Using Pareto points for model identification in predictive toxicology. Journal of Cheminformatics. 5: 16.
    Link to publisher’s version
    http://dx.doi.org/10.1186/1758-2946-5-16
    Type
    Journal Article
    Collections
    Engineering and Informatics Publications

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