scholarly journals Self-Organizing Map Analysis of Toxicity-Related Cell Signaling Pathways for Metal and Metal Oxide Nanoparticles

2011 ◽  
Vol 45 (4) ◽  
pp. 1695-1702 ◽  
Author(s):  
Robert Rallo ◽  
Bryan France ◽  
Rong Liu ◽  
Sumitra Nair ◽  
Saji George ◽  
...  
Keyword(s):  
Metal Oxide ◽  
Cell Signaling ◽  
Signaling Pathways ◽  
Metal Oxide Nanoparticles ◽  
Oxide Nanoparticles ◽  
Self Organizing Map ◽  
Related Cell ◽  
Map Analysis ◽  
Cell Signaling Pathways ◽  
Self Organizing

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

2017 ◽  
Author(s):  
Natalia Sizochenko ◽  
Alicja Mikolajczyk ◽  
Karolina Jagiello ◽  
Tomasz Puzyn ◽  
Jerzy Leszczynski ◽  
...  
Keyword(s):  
Missing Values ◽  
Metal Oxide Nanoparticles ◽  
Machine Learning Techniques ◽  
Self Organizing Map ◽  
Related Data ◽  
Mammalian Cell Lines ◽  
Learning Techniques ◽  
Combination Of Methods ◽  
Map Analysis ◽  
Unique Chemical

Application of predictive modeling approaches is able solve the problem of the missing data. There are a lot of studies that investigate the effects of missing values on qualitative or quantitative modeling, but only few publications have been<br>discussing it in case of applications to nanotechnology related data. Current project aimed at the development of multi-nano-read-across modeling technique that helps in predicting the toxicity of different species: bacteria, algae, protozoa, and mammalian cell lines. In this study, the experimental toxicity for 184 metal- and silica oxides (30 unique chemical types) nanoparticles from 15 experimental datasets was analyzed. A hybrid quantitative multi-nano-read-across approach that combines interspecies correlation analysis and self-organizing map analysis was developed. At the first step, hidden patterns of toxicity among the nanoparticles were identified using a combination of methods. Then the developed model that based on categorization of metal oxide nanoparticles’ toxicity outcomes was evaluated by means of combination of supervised and unsupervised machine learning techniques to find underlying factors responsible for toxicity.

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

2017 ◽  
Author(s):  
Natalia Sizochenko ◽  
Alicja Mikolajczyk ◽  
Karolina Jagiello ◽  
Tomasz Puzyn ◽  
Jerzy Leszczynski ◽  
...  
Keyword(s):  
Missing Values ◽  
Metal Oxide Nanoparticles ◽  
Machine Learning Techniques ◽  
Self Organizing Map ◽  
Related Data ◽  
Mammalian Cell Lines ◽  
Learning Techniques ◽  
Combination Of Methods ◽  
Map Analysis ◽  
Unique Chemical

Application of predictive modeling approaches is able solve the problem of the missing data. There are a lot of studies that investigate the effects of missing values on qualitative or quantitative modeling, but only few publications have been<br>discussing it in case of applications to nanotechnology related data. Current project aimed at the development of multi-nano-read-across modeling technique that helps in predicting the toxicity of different species: bacteria, algae, protozoa, and mammalian cell lines. In this study, the experimental toxicity for 184 metal- and silica oxides (30 unique chemical types) nanoparticles from 15 experimental datasets was analyzed. A hybrid quantitative multi-nano-read-across approach that combines interspecies correlation analysis and self-organizing map analysis was developed. At the first step, hidden patterns of toxicity among the nanoparticles were identified using a combination of methods. Then the developed model that based on categorization of metal oxide nanoparticles’ toxicity outcomes was evaluated by means of combination of supervised and unsupervised machine learning techniques to find underlying factors responsible for toxicity.

Star fruit extract-mediated green synthesis of metal oxide nanoparticles

2021 ◽  
pp. 1-8
Author(s):  
Sagadevan Suresh ◽  
Selvaraj Vennila ◽  
J. Anita Lett ◽  
Is Fatimah ◽  
Faruq Mohammad ◽  
...  
Keyword(s):  
Metal Oxide ◽  
Green Synthesis ◽  
Metal Oxide Nanoparticles ◽  
Oxide Nanoparticles ◽  
Fruit Extract ◽  
Star Fruit

Quantum mechanics descriptors in a nano-QSAR model to predict metal oxide nanoparticles toxicity in human keratinous cells

2021 ◽  
Vol 23 (8) ◽  
Author(s):  
Eliecer Peláez Sifonte ◽  
Fidel Antonio Castro-Smirnov ◽  
Argenis Adrian Soutelo Jimenez ◽  
Héctor Raúl González Diez ◽  
Fernando Guzmán Martínez
Keyword(s):  
Quantum Mechanics ◽  
Metal Oxide ◽  
Metal Oxide Nanoparticles ◽  
Qsar Model ◽  
Oxide Nanoparticles ◽  
Nanoparticles Toxicity
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