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Comparing the CORAL and random forest approaches for modelling the in vitro cytotoxicity of silica nanomaterials

Cassano, A and Marchese Robinson, RL and Palczewska, A and Puzyn, T and Gajewicz, A and Tran, L and Manganelli, S and Cronin, MTD (2016) Comparing the CORAL and random forest approaches for modelling the in vitro cytotoxicity of silica nanomaterials. Alternatives to the Laboratory Animals, 44 (6). ISSN 0261-1929

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Abstract

Nanotechnology is one of the most important technological developments of the twenty-first century. In silico methods such as quantitative structure-activity relationships (QSARs) to predict toxicity promote the safe-by-design approach for the development of new materials, including nanomaterials. In this study, a set of cytotoxicity experimental data corresponding to 19 data points for silica nanomaterials was investigated to compare the widely employed CORAL and Random Forest approaches in terms of their usefulness for developing so-called “nano-QSAR” models. “External” leave-one-out cross-validation (LOO) analysis was performed to validate the two different approaches. An analysis of variable importance measures and signed feature contributions for both algorithms was undertaken in order to interpret the models developed. CORAL showed a more pronounced difference between the average coefficient of determination (R2) between training and LOO (0.83 and 0.65 for training and LOO respectively) compared to Random Forest (0.87 and 0.78 without bootstrap sampling, 0.90 and 0.78 with bootstrap sampling), which may be due to overfitting. The aspect ratio and zeta potential from amongst the nanomaterials’ physico-chemical properties were found to be the two most important variables for the Random Forest and the average feature contributions calculated for the corresponding descriptors were consistent with the clear trends observed in the dataset: less negative zeta potential values and lower aspect ratio values were associated with higher cytotoxicity. In contrast, CORAL failed to capture these trends.

Item Type: Article
Uncontrolled Keywords: 0707 Veterinary Sciences
Subjects: Q Science > QD Chemistry
Q Science > QH Natural history > QH301 Biology
Divisions: Pharmacy & Biomolecular Sciences
Publisher: Fund for the Replacement of Animals in Medical Experiments
Date Deposited: 25 Jan 2017 10:10
Last Modified: 07 Sep 2017 16:50
URI: http://researchonline.ljmu.ac.uk/id/eprint/5267

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