datasets

RDF version of the data from Hagar I. Labouta et al. Meta-Analysis of Nanoparticle Cytotoxicity via Data-Mining the Literature. NanoImpact (2019)

Original Study Abstract

Developing predictive modeling frameworks of potential cytotoxicity of engineered nanoparticles is critical for environmental and health risk analysis. The complexity and the heterogeneity of available data on potential risks of nanoparticles, in addition to interdependency of relevant influential attributes, makes it challenging to develop a generalization of nanoparticle toxicity behavior. Lack of systematic approaches to investigate these risks further adds uncertainties and variability to the body of literature and limits generalizability of existing studies. Here, we developed a rigorous approach for assembling published evidence on cytotoxicity of several organic and inorganic nanoparticles and unraveled hidden relationships that were not targeted in the original publications. We used a machine learning approach that employs decision trees together with feature selection algorithms (e.g., Gain ratio) to analyze a set of published nanoparticle cytotoxicity sample data (2896 samples). The specific studies were selected because they specified nanoparticle-, cell-, and screening method-related attributes. The resultant decision-tree classifiers are sufficiently simple, accurate, and with high prediction power and should be widely applicable to a spectrum of nanoparticle cytotoxicity settings. Among several influential attributes, we show that the cytotoxicity of nanoparticles is primarily predicted from the nanoparticle material chemistry, followed by nanoparticle concentration and size, cell type, and cytotoxicity screening indicator. Overall, our study indicates that following rigorous and transparent methodological experimental approaches, in parallel to continuous addition to this data set developed using our approach, will offer higher predictive power and accuracy and uncover hidden relationships. Results obtained in this study help focus future studies to develop nanoparticles that are safe by design. [Source: https://doi.org/10.1021/acsnano.8b07562]

Data Sample

nanoparticle type_organic_inorganic coat diameter dose zeta_potential cell_line cell_line_or_primary human_or_animal animal_species cell_morphology cell_age_embryonic_or_adult cell_organ_or_tissue exposure_time test_assay test_indicator biochemical_metric cell_viability inference_checked_y_n colloidal_stability_checked_y_n positive_control_y_n publication_year particle_id reference_doi row_num
CeO2 I   20.3 0.002477828766   L929 L A Mouse Fibroblast A Areolar tissue 24 MTT tetrazolium salt cell metabolic activity 105.58918 N N N 2015 1 10.1016/j.ceramint.2014.09.095 1
CeO2 I   20.3 0.004955657533   L929 L A Mouse Fibroblast A Areolar tissue 24 MTT tetrazolium salt cell metabolic activity 103.02621 N N N 2015 1 10.1016/j.ceramint.2014.09.095 2
CeO2 I   20.3 0.009911315066   L929 L A Mouse Fibroblast A Areolar tissue 24 MTT tetrazolium salt cell metabolic activity 104.85748 N N N 2015 1 10.1016/j.ceramint.2014.09.095 3
CeO2 I   20.3 0.01982263013   L929 L A Mouse Fibroblast A Areolar tissue 24 MTT tetrazolium salt cell metabolic activity 100.0967 N N N 2015 1 10.1016/j.ceramint.2014.09.095 4
CeO2 I   20.3 0.03964526026   L929 L A Mouse Fibroblast A Areolar tissue 24 MTT tetrazolium salt cell metabolic activity 97.53237 N N N 2015 1 10.1016/j.ceramint.2014.09.095 5
Se I   79.6 0.000001307545634 0 PC3 L H   Epithelial A Prostate 24 XTT tetrazolium salt cell metabolic activity 91.09037 N N N 2014 2 10.4172/2157-7439.1000194 6
Se I   79.6 0.000002615091268 0 PC3 L H   Epithelial A Prostate 24 XTT tetrazolium salt cell metabolic activity 77.62113 N N N 2014 2 10.4172/2157-7439.1000194 7
Se I   79.6 0.000005230182535 0 PC3 L H   Epithelial A Prostate 24 XTT tetrazolium salt cell metabolic activity 54.2135 N N N 2014 2 10.4172/2157-7439.1000194 8
Se I   79.6 0.000007845273803 0 PC3 L H   Epithelial A Prostate 24 XTT tetrazolium salt cell metabolic activity 44.062363 N N N 2014 2 10.4172/2157-7439.1000194 9
CuO I   52.51 0.00003471326159 -39.67 A549 L H   Epithelial A Lung 24 MTT tetrazolium salt cell metabolic activity 74.58716 N N N 2010 3 10.1016/j.bbrc.2010.04.156 10

Data Summary

Group Count
# of Materials 118
# of Assays 267
# of Measurement groups 354
# of Endpoints 6061
# of Nanomaterial types 33
# of Assay types 3
# of Endpoint types 3
# of Units 5
# of Species 8
This site is open source. Improve this page.