Predicting Cytotoxicity of Metal Oxide Nanoparticles using Isalos Analytics Platform.
Isalos analytics platform
atomistic descriptors
computational descriptors
cytotoxicity
in silico modelling
machine learning
metal oxide nanoparticles
Journal
Nanomaterials (Basel, Switzerland)
ISSN: 2079-4991
Titre abrégé: Nanomaterials (Basel)
Pays: Switzerland
ID NLM: 101610216
Informations de publication
Date de publication:
13 Oct 2020
13 Oct 2020
Historique:
received:
13
09
2020
revised:
03
10
2020
accepted:
07
10
2020
entrez:
17
10
2020
pubmed:
18
10
2020
medline:
18
10
2020
Statut:
epublish
Résumé
A literature curated dataset containing 24 distinct metal oxide (Me
Identifiants
pubmed: 33066094
pii: nano10102017
doi: 10.3390/nano10102017
pmc: PMC7601995
pii:
doi:
Types de publication
Journal Article
Langues
eng
Subventions
Organisme : Research and Innovation Foundation
ID : POST-DOC/0718/0070
Organisme : European Union's Horizon 2020 research and innovation programme via NanoSolveIT Project
ID : 814572
Déclaration de conflit d'intérêts
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript or in the decision to publish the results.
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