Nanosafety-data-reusability-34-datasets

Predicting In Vitro Neurotoxicity Induced by Nanoparticles Using Machine Learning (2020)

• Authors: Irini Furxhi, Finbarr Murphy
• Journal: International Journal of Molecular Sciences
• Article Type: journal-article
• Dataset Type: Toxicological Datasets
• Subject: Inorganic Chemistry,Organic Chemistry,Physical and Theoretical Chemistry,Computer Science Applications,Spectroscopy,Molecular Biology,General Medicine,Catalysis
• Date: 2020-7-25
• License: https://creativecommons.org/licenses/by/4.0/
• URL: http://dx.doi.org/10.3390/ijms21155280
• DOI: 10.3390/ijms21155280

Original Study Abstract

The practice of non-testing approaches in nanoparticles hazard assessment is necessary to identify and classify potential risks in a cost effective and timely manner. Machine learning techniques have been applied in the field of nanotoxicology with encouraging results. A neurotoxicity classification model for diverse nanoparticles is presented in this study. A data set created from multiple literature sources consisting of nanoparticles physicochemical properties, exposure conditions and in vitro characteristics is compiled to predict cell viability. Pre-processing techniques were applied such as normalization methods and two supervised instance methods, a synthetic minority over-sampling technique to address biased predictions and production of subsamples via bootstrapping. The classification model was developed using random forest and goodness-of-fit with additional robustness and predictability metrics were used to evaluate the performance. Information gain analysis identified the exposure dose and duration, toxicological assay, cell type, and zeta potential as the five most important attributes to predict neurotoxicity in vitro. This is the first tissue-specific machine learning tool for neurotoxicity prediction caused by nanoparticles in in vitro systems. The model performs better than non-tissue specific models.

Data Sample

Reference	Study title	DOI	Dose	Time	Zeta_W	Zeta_W measurement	Zeta_M	Zeta_M measurement	Hydro_W	Hydro_size_W measrement	Hydro_M	Hydro_size_M measrement	Size	Size measurement	SSA	Surf_area measurement	NP type	shape	Shape measurement	C_Origin	C_name	C_Type	Assay	Cell viability
Trickler et al., 2012	Effects of copper nanoparticles on rat cerebral microvessel endothelial cells.	10.2217/nnm.11.154	10	4	-47.6	LDV	?	?	?	?	?	?	40	TEM	?	?	CuO	?		Rat	BMEC	Endothelial	XTT	Toxic
Trickler et al., 2012	Effects of copper nanoparticles on rat cerebral microvessel endothelial cells.	10.2217/nnm.11.154	10	4	-36.6	LDV	?	?	?	?	?	?	60	TEM	?	?	CuO	?	?	Rat	BMEC	Endothelial	XTT	Toxic
Trickler et al., 2012	Effects of copper nanoparticles on rat cerebral microvessel endothelial cells.	10.2217/nnm.11.154	1,56	24	-47.6	LDV	?	?	?	?	?	?	40	TEM	?	?	CuO	?	?	Rat	BMEC	Endothelial	XTT	Toxic
Trickler et al., 2012	Effects of copper nanoparticles on rat cerebral microvessel endothelial cells.	10.2217/nnm.11.154	1,56	24	-36.6	LDV	?	?	?	?	?	?	60	TEM	?	?	CuO	?	?	Rat	BMEC	Endothelial	XTT	Toxic
Trickler et al., 2012	Effects of copper nanoparticles on rat cerebral microvessel endothelial cells.	10.2217/nnm.11.154	3,13	24	-47.6	LDV	?	?	?	?	?	?	40	TEM	?	?	CuO	?	?	Rat	BMEC	Endothelial	XTT	Toxic

Data Summary

Variable	Count (unique values)
Study title	32
DOI	32
Dose	32
Time	58
Zeta_W	15
Zeta_W measurement	27
Zeta_M measurement	30
Hydro_W	5
Hydro_size_W measrement	25
Hydro_M	2
Hydro_size_M measrement	26
Size	2
Size measurement	35
SSA	4
Surf_area measurement	9
shape	9
Shape measurement	10
C_name	3
C_Type	14
Assay	9
Cell viability	14

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