The way to cover prediction for cytotoxicity for all existing nano-sized metal oxides by using neural network method
Published 2017 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
The way to cover prediction for cytotoxicity for all existing nano-sized metal oxides by using neural network method
Authors
Keywords
-
Journal
Nanotoxicology
Volume 11, Issue 4, Pages 475-483
Publisher
Informa UK Limited
Online
2017-03-23
DOI
10.1080/17435390.2017.1310949
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- International regulatory needs for development ofan IATA for non-genotoxic carcinogenic chemical substances
- (2016) Miriam Jacobs ALTEX-Alternatives to Animal Experimentation
- Toxicity of Metal Oxide Nanoparticles in Escherichia coli Correlates with Conduction Band and Hydration Energies
- (2015) Chitrada Kaweeteerawat et al. ENVIRONMENTAL SCIENCE & TECHNOLOGY
- Periodic table-based descriptors to encode cytotoxicity profile of metal oxide nanoparticles: A mechanistic QSTR approach
- (2014) Supratik Kar et al. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY
- Computational ecotoxicology: Simultaneous prediction of ecotoxic effects of nanoparticles under different experimental conditions
- (2014) Valeria V. Kleandrova et al. ENVIRONMENT INTERNATIONAL
- 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
- (2014) Valeria V. Kleandrova et al. ENVIRONMENTAL SCIENCE & TECHNOLOGY
- Optimal nano-descriptors as translators of eclectic data into prediction of the cell membrane damage by means of nano metal-oxides
- (2014) Alla P. Toropova et al. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
- Computer-aided nanotoxicology: assessing cytotoxicity of nanoparticles under diverse experimental conditions by using a novel QSTR-perturbation approach
- (2014) Feng Luan et al. Nanoscale
- From basic physics to mechanisms of toxicity: the “liquid drop” approach applied to develop predictive classification models for toxicity of metal oxide nanoparticles
- (2014) Natalia Sizochenko et al. Nanoscale
- Novel approach for efficient predictions properties of large pool of nanomaterials based on limited set of species: nano-read-across
- (2014) Agnieszka Gajewicz et al. NANOTECHNOLOGY
- Towards understanding mechanisms governing cytotoxicity of metal oxides nanoparticles: Hints from nano-QSAR studies
- (2014) Agnieszka Gajewicz et al. Nanotoxicology
- Nano-QSAR modeling for predicting biological activity of diverse nanomaterials
- (2014) Kunwar P. Singh et al. RSC Advances
- Using experimental data of Escherichia coli to develop a QSAR model for predicting the photo-induced cytotoxicity of metal oxide nanoparticles
- (2013) Kavitha Pathakoti et al. JOURNAL OF PHOTOCHEMISTRY AND PHOTOBIOLOGY B-BIOLOGY
- Novel application of the CORAL software to model cytotoxicity of metal oxide nanoparticles to bacteria Escherichia coli
- (2012) Andrey A. Toropov et al. CHEMOSPHERE
- Concerning electronegativity as a basic elemental property and why the periodic table is usually represented in its medium form
- (2012) Mark R. Leach Foundations of Chemistry
- Evaluating the applicability domain in the case of classification predictive models for carcinogenicity based on the counter propagation artificial neural network
- (2011) Natalja Fjodorova et al. JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN
- Using nano-QSAR to predict the cytotoxicity of metal oxide nanoparticles
- (2011) Tomasz Puzyn et al. Nature Nanotechnology
- In vitro evaluation of cytotoxicity of engineered metal oxide nanoparticles
- (2009) Xiaoke Hu et al. SCIENCE OF THE TOTAL ENVIRONMENT
- Toward the Development of âNano-QSARsâ: Advances and Challenges
- (2009) Tomasz Puzyn et al. Small
- Chemical stability of metallic nanoparticles: A parameter controlling their potential cellular toxicity in vitro
- (2008) Mélanie Auffan et al. ENVIRONMENTAL POLLUTION
- Verification of the geological origin of bottled mineral water using artificial neural networks
- (2008) Neva Grošelj et al. FOOD CHEMISTRY
Add your recorded webinar
Do you already have a recorded webinar? Grow your audience and get more views by easily listing your recording on Peeref.
Upload NowCreate your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create Now