How the toxicity of nanomaterials towards different species could be simultaneously evaluated: a novel multi-nano-read-across approach

Natalia Sizochenko , Alicja Mikołajczyk , Karolina Jagiełło , Tomasz Puzyn , Jerzy Leszczynski , Bakhtiyor Rasulev


Application of predictive modeling approaches can solve the problem of missing data. Numerous studies have investigated the effects of missing values on qualitative or quantitative modeling, but only a few studies have discussed it for the case of applications in nanotechnology-related data. The present study is aimed at the development of a multi-nano-read-across modeling technique that helps in predicting the toxicity of different species such as bacteria, algae, protozoa, and mammalian cell lines. Herein, the experimental toxicity of 184 metal and silica oxide (30 unique chemical types) nanoparticles from 15 datasets is analyzed. A hybrid quantitative multi-nano-read-across approach that combines interspecies correlation analysis and self-organizing map analysis is developed. In the first step, hidden patterns of toxicity among nanoparticles are identified using a combination of methods. Subsequently, the developed model based on categorization of the toxicity of the metal oxide nanoparticle outcomes is evaluated via the combination of supervised and unsupervised machine learning techniques to determine the underlying factors responsible for the toxicity.
Author Natalia Sizochenko PChŚ
Natalia Sizochenko,,
- Laboratory of Environmental Chemometrics
, Alicja Mikołajczyk PChŚ
Alicja Mikołajczyk,,
- Laboratory of Environmental Chemometrics
, Karolina Jagiełło PChŚ
Karolina Jagiełło,,
- Laboratory of Environmental Chemometrics
, Tomasz Puzyn PChŚ
Tomasz Puzyn,,
- Laboratory of Environmental Chemometrics
, Jerzy Leszczynski
Jerzy Leszczynski,,
, Bakhtiyor Rasulev
Bakhtiyor Rasulev,,
Journal seriesNanoscale, ISSN 2040-3364
Issue year2018
Publication size in sheets0.5
Languageen angielski
Score (nominal)40
ScoreMinisterial score = 40.0, 11-04-2018, ArticleFromJournal
Ministerial score (2013-2016) = 40.0, 11-04-2018, ArticleFromJournal
Publication indicators WoS Impact Factor: 2016 = 7.367 (2) - 2016=7.668 (5)
Citation count*0
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* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.