Toxicity Classification of Oxide Nanomaterials: Effects of Data Gap Filling and PChem Score-based Screening Approaches
Autor: | My Kieu Ha, Tae Hyun Yoon, Choi Jang-Sik, Tung X. Trinh, Desy Maulina, Hyung Gi Byun |
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Jazyk: | angličtina |
Rok vydání: | 2018 |
Předmět: |
Computer science
Engineered nanomaterials Nanoparticle lcsh:Medicine 02 engineering and technology 010402 general chemistry Machine learning computer.software_genre 01 natural sciences Article Nanomaterials lcsh:Science Gap filling Multidisciplinary business.industry lcsh:R 021001 nanoscience & nanotechnology 0104 chemical sciences Nanotoxicology Toxicity lcsh:Q Artificial intelligence 0210 nano-technology business Engineering sciences. Technology computer Applicability domain |
Zdroj: | Scientific Reports, Vol 8, Iss 1, Pp 1-11 (2018) Scientific Reports Scientific reports |
ISSN: | 2045-2322 |
Popis: | Development of nanotoxicity prediction models is becoming increasingly important in the risk assessment of engineered nanomaterials. However, it has significant obstacles caused by the wide heterogeneities of published literature in terms of data completeness and quality. Here, we performed a meta-analysis of 216 published articles on oxide nanoparticles using 14 attributes of physicochemical, toxicological and quantum-mechanical properties. Particularly, to improve completeness and quality of the extracted dataset, we adapted two preprocessing approaches: data gap-filling and physicochemical property based scoring. Performances of nano-SAR classification models revealed that the dataset with the highest score value resulted in the best predictivity with compromise in its applicability domain. The combination of physicochemical and toxicological attributes was proved to be more relevant to toxicity classification than quantum-mechanical attributes. Overall, by adapting these two preprocessing methods, we demonstrated that meta-analysis of nanotoxicity literatures could provide an effective alternative for the risk assessment of engineered nanomaterials. |
Databáze: | OpenAIRE |
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