Abstract
Computational prioritization of chemicals for potential skin sensitization risks plays essential roles in the risk assessment of environmental chemicals and drug development. Given the huge number of chemicals for testing, computational methods enable the fast identification of high-risk chemicals for experimental validation and design of safer alternatives. However, the development of robust prediction model requires a large dataset of tested chemicals that is usually not available for most toxicological endpoints, especially for human data. A small training dataset makes the development of effective models difficult with insufficient coverage and accuracy. In this study, an ensemble tree-based multitask learning method was developed incorporating three relevant tasks in the well-defined adverse outcome pathway (AOP) of skin sensitization to transfer shared knowledge to the major task of human sensitizers. The results show both largely improved coverage and accuracy compared with three state-of-the-art methods. A user-friendly prediction server was available at https://cwtung.kmu.edu.tw/skinsensdb/predict. As AOPs for various toxicity endpoints are being actively developed, the proposed method can be applied to develop prediction models for other endpoints.
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Data availability
SkinSensPred and datasets are freely available at https://cwtung.kmu.edu.tw/skinsensdb.
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Acknowledgements
This work was supported by Ministry of Science and Technology of Taiwan [MOST104-2221-E-037-001-MY3, MOST107-2221-E-037-005-MY3]; National Health Research Institutes [NHRI-107A1-EMCO-0318184]; and Research Center for Environmental Medicine in Kaohsiung Medical University from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan. This work was initiated in Kaohsiung Medical University and completed in Taipei Medical University. The funding agencies play no role in the study design, data analysis and manuscript preparation.
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Tung, CW., Lin, YH. & Wang, SS. Transfer learning for predicting human skin sensitizers. Arch Toxicol 93, 931–940 (2019). https://doi.org/10.1007/s00204-019-02420-x
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DOI: https://doi.org/10.1007/s00204-019-02420-x