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Practices and Trends of Machine Learning Application in Nanotoxicology

Department of Accounting and Finance, Kemmy Business School, University of Limerick, V94PH93 Limerick, Ireland
Transgero Limited, Newcastle, V42V384 Limerick, Ireland
Department of Mechanical Engineering, Environmental Informatics Research Group, Aristotle University of Thessaloniki, 54124 Thessaloniki Box 483, Greece
ELEGI/Colt Laboratory, Queen’s Medical Research Institute, 47 Little France Crescent, University of Edinburgh, Edinburgh EH16 4TJ, Scotland, UK
Author to whom correspondence should be addressed.
Nanomaterials 2020, 10(1), 116;
Received: 28 November 2019 / Revised: 31 December 2019 / Accepted: 6 January 2020 / Published: 8 January 2020
(This article belongs to the Special Issue From Nanoinformatics to Nanomaterials Risk Assessment and Governance)
Machine Learning (ML) techniques have been applied in the field of nanotoxicology with very encouraging results. Adverse effects of nanoforms are affected by multiple features described by theoretical descriptors, nano-specific measured properties, and experimental conditions. ML has been proven very helpful in this field in order to gain an insight into features effecting toxicity, predicting possible adverse effects as part of proactive risk analysis, and informing safe design. At this juncture, it is important to document and categorize the work that has been carried out. This study investigates and bookmarks ML methodologies used to predict nano (eco)-toxicological outcomes in nanotoxicology during the last decade. It provides a review of the sequenced steps involved in implementing an ML model, from data pre-processing, to model implementation, model validation, and applicability domain. The review gathers and presents the step-wise information on techniques and procedures of existing models that can be used readily to assemble new nanotoxicological in silico studies and accelerates the regulation of in silico tools in nanotoxicology. ML applications in nanotoxicology comprise an active and diverse collection of ongoing efforts, although it is still in their early steps toward a scientific accord, subsequent guidelines, and regulation adoption. This study is an important bookend to a decade of ML applications to nanotoxicology and serves as a useful guide to further in silico applications. View Full-Text
Keywords: machine learning; in silico; computational; nanoparticle; nanoforms; nanotoxicology machine learning; in silico; computational; nanoparticle; nanoforms; nanotoxicology
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MDPI and ACS Style

Furxhi, I.; Murphy, F.; Mullins, M.; Arvanitis, A.; Poland, C.A. Practices and Trends of Machine Learning Application in Nanotoxicology. Nanomaterials 2020, 10, 116.

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