An ISA-TAB-Nano based data collection framework to support data-driven modelling of nanotoxicology (Q27942681)

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2 March 2020

title

An ISA-TAB-Nano based data collection framework to support data-driven modelling of nanotoxicology (English)

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Modelling properties, interactions, toxicity and environmental behaviour of engineered nanoparticles

2 March 2020

sponsor

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1 reference

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Richard L Marchese Robinson

1

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2 March 2020

Andrea-Nicole Richarz

3

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2 March 2020

author name string

Mark T D Cronin

2

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2 March 2020

Robert Rallo

4

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2 March 2020

language of work or name

English

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publication date

5 October 2015

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2 March 2020

number of pages

22

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Beilstein Journal of Nanotechnology

inferred from page(s)

published in

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2 March 2020

volume

6

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2 March 2020

page(s)

1978-1999

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The eNanoMapper database for nanomaterial safety information

2 March 2020

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linkedISA: semantic representation of ISA-Tab experimental metadata

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Circumventing the Crabtree Effect: Replacing Media Glucose with Galactose Increases Susceptibility of HepG2 Cells to Mitochondrial Toxicants

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Cytotoxicity of nanoparticles

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Experiences in supporting the structured collection of cancer nanotechnology data using caNanoLab

7 April 2017

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29 September 2017

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Genotoxicity of metal oxide nanomaterials: review of recent data and discussion of possible mechanisms

29 September 2017

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Optimal descriptor as a translator of eclectic data into prediction of cytotoxicity for metal oxide nanoparticles under different conditions.

29 September 2017

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Computational tool for risk assessment of nanomaterials: novel QSTR-perturbation model for simultaneous prediction of ecotoxicity and cytotoxicity of uncoated and coated nanoparticles under multiple experimental conditions.

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Computer-aided nanotoxicology: assessing cytotoxicity of nanoparticles under diverse experimental conditions by using a novel QSTR-perturbation approach.

29 September 2017

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Using experimental data of Escherichia coli to develop a QSAR model for predicting the photo-induced cytotoxicity of metal oxide nanoparticles

29 September 2017

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29 September 2017

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29 September 2017

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29 September 2017

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29 September 2017

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29 September 2017

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29 September 2017

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29 September 2017

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29 September 2017

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29 September 2017

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Novel approach for efficient predictions properties of large pool of nanomaterials based on limited set of species: nano-read-across

29 September 2017

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2 June 2018

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Quasi-QSAR for mutagenic potential of multi-walled carbon-nanotubes

2 June 2018

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Nanoparticle surface characterization and clustering through concentration-dependent surface adsorption modeling

27 November 2018

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27 November 2018

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Novel application of the CORAL software to model cytotoxicity of metal oxide nanoparticles to bacteria Escherichia coli

27 November 2018

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27 November 2018

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27 November 2018

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27 November 2018

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27 November 2018

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12 December 2020

Photocatalytic Production of H2O2 and Organic Peroxides on Quantum-Sized Semiconductor Colloids

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12 December 2020

Characterization of the size, shape, and state of dispersion of nanoparticles for toxicological studies

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5 February 2023

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A strategy for grouping of nanomaterials based on key physico-chemical descriptors as a basis for safer-by-design NMs

1 reference

5 February 2023

https://opencitations.net/index/coci/api/v1/references/10.3762/BJNANO.6.202

Nanomaterial registry: database that captures the minimal information about nanomaterial physico-chemical characteristics

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5 February 2023

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Development of computational models for the prediction of the toxicity of nanomaterials

0 references

Identifiers

DOI

10.3762/BJNANO.6.202

2 references

https://www.ebi.ac.uk/europepmc/webservices/rest/search?query=EXT_ID:26665069 AND SRC:MED&resulttype=core&format=json

2 March 2020

1 reference

2 references

https://www.ebi.ac.uk/europepmc/webservices/rest/search?query=EXT_ID:26665069 AND SRC:MED&resulttype=core&format=json

2 March 2020

PubMed publication ID

26665069

2 references

https://www.ebi.ac.uk/europepmc/webservices/rest/search?query=EXT_ID:26665069 AND SRC:MED&resulttype=core&format=json