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Supporting Information
for 
Identifying diverse metal oxide nanomaterials with lethal effects on embryonic zebrafish using Machine Learning
R. L. Marchese Robinson,1 H. Sarimveis,2 P. Doganis,2 X. Jia,1 M. Kotzabasaki,2 C. Gousiadou,2 S.L. Harper,3,4,5 T. A.  Wilkins1,*

1.School of Chemical and Process Engineering, University of Leeds, Leeds, LS2 9JT, United Kingdom
2. School of Chemical Engineering, National Technical University of Athens, 9 Heroon Polytechniou str. Zografou Campus, 15780 Athens, Greece
3. School of Chemical, Biological, and Environmental Engineering, Oregon State University, Corvallis, Oregon, USA
4. Department of Environmental and Molecular Toxicology, Oregon State University, Corvallis, Oregon, USA
5. Oregon Nanoscience and Microtechnologies Institute, Eugene, Oregon, USA
*Corresponding author: T.A.Wilkins@leeds.ac.uk
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These data are distributed under the terms of the Creative Commons Attribution License Version 4.0	(CC BY 4.0) license. https://creativecommons.org/licenses/by/4.0/.
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Literature reference:

Kleandrova et al. 2014, Computational ecotoxicology: Simultaneous prediction of ecotoxic effects of nanoparticles under different experimental conditions, Environment International 73 (2014) 288–294

doi:10.1016/j.envint.2014.08.009
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Descriptor input column: Core Atomic Composition

Endpoint column: Endpoint Toxicity Classification

N.B. Given that different timepoints are considered, is this really a suitable external test? 

Perhaps the raw data reported in the primary experimental references would be more suitable for expanding the external test data?



