review article | Q7318358 |
scholarly article | Q13442814 |
P356 | DOI | 10.3109/17435390.2014.952698 |
P8608 | Fatcat ID | release_isljowzh2rdwzl4rxutlskmx2i |
P953 | full work available at URL | http://eprints.whiterose.ac.uk/93093/ |
P698 | PubMed publication ID | 25211549 |
P1154 | Scopus EID | 2-s2.0-84937933471 |
P50 | author | CaiYun Ma | Q53843828 |
Tomasz Puzyn | Q41728850 | ||
P2093 | author name string | Jian Wang | |
Xue Z Wang | |||
Ceyda Oksel | |||
Terry Wilkins | |||
Kenneth N Robinson | |||
Ratna Tantra | |||
Cai Y Ma | |||
P2860 | cites work | Summary of a workshop on regulatory acceptance of (Q)SARs for human health and environmental endpoints. | Q24815747 |
Use of QSARs in international decision-making frameworks to predict ecologic effects and environmental fate of chemical substances | Q24815768 | ||
Use of QSARs in international decision-making frameworks to predict health effects of chemical substances | Q24815781 | ||
Nanoinformatics: emerging databases and available tools | Q27008517 | ||
Quantitative nanostructure-activity relationship modeling | Q28385022 | ||
Beware of q2! | Q28842863 | ||
Principal component and causal analysis of structural and acute in vitro toxicity data for nanoparticles | Q30616632 | ||
Evaluation criteria for the quality of published experimental data on nanomaterials and their usefulness for QSAR modelling | Q30710110 | ||
Using experimental data of Escherichia coli to develop a QSAR model for predicting the photo-induced cytotoxicity of metal oxide nanoparticles | Q30722652 | ||
Application of neural networks to large dataset QSAR, virtual screening, and library design | Q30858526 | ||
Megavariate analysis of environmental QSAR data. Part I--a basic framework founded on principal component analysis (PCA), partial least squares (PLS), and statistical molecular design (SMD). | Q31044061 | ||
Decision tree SAR models for developmental toxicity based on an FDA/TERIS database | Q47912674 | ||
Predictivity and reliability of QSAR models: the case of mutagens and carcinogens. | Q50065385 | ||
Nanoparticle dispersity in toxicology. | Q51531075 | ||
Nanomaterials for environmental studies: classification, reference material issues, and strategies for physico-chemical characterisation. | Q51645271 | ||
Interpreting computational neural network QSAR models: a measure of descriptor importance. | Q51972689 | ||
Comparative study of QSAR/QSPR correlations using support vector machines, radial basis function neural networks, and multiple linear regression. | Q51993081 | ||
Application of QSARs in risk management of existing chemicals. | Q52383989 | ||
Structure-activity relationship analysis tools: validation and applicability in predicting carcinogens. | Q53326943 | ||
Endotoxin contamination of engineered nanomaterials | Q53460338 | ||
Glossary of terms used in toxicology, 2nd edition (IUPAC Recommendations 2007) | Q56908044 | ||
PCA-Based Representations of Graphs for Prediction in QSAR Studies | Q56979624 | ||
Nano-quantitative structure–activity relationship modeling using easily computable and interpretable descriptors for uptake of magnetofluorescent engineered nanoparticles in pancreatic cancer cells | Q57008918 | ||
Quantitative structure-activity relationship models for ready biodegradability of chemicals | Q31112383 | ||
Rational selection of training and test sets for the development of validated QSAR models | Q31165909 | ||
Computational methods in developing quantitative structure-activity relationships (QSAR): a review | Q33236244 | ||
Robust cross-validation of linear regression QSAR models | Q33372866 | ||
Molecular determinants of juvenile hormone action as revealed by 3D QSAR analysis in Drosophila | Q33472678 | ||
Use of a (quantitative) structure-activity relationship [(Q)SAR] model to predict the toxicity of naphthenic acids | Q33524445 | ||
Classification NanoSAR development for cytotoxicity of metal oxide nanoparticles | Q33859712 | ||
The practice of structure activity relationships (SAR) in toxicology | Q33951674 | ||
Dependence of QSAR models on the selection of trial descriptor sets: a demonstration using nanotoxicity endpoints of decorated nanotubes. | Q34517977 | ||
Anisotropy of building blocks and their assembly into complex structures | Q34658244 | ||
A comparative study of submicron particle sizing platforms: accuracy, precision and resolution analysis of polydisperse particle size distributions | Q34768767 | ||
The expanding role of predictive toxicology: an update on the (Q)SAR models for mutagens and carcinogens | Q36761869 | ||
Use and perceived benefits and barriers of QSAR models for REACH: findings from a questionnaire to stakeholders | Q37166513 | ||
Toward the development of "nano-QSARs": advances and challenges | Q37605014 | ||
Exploring quantitative nanostructure-activity relationships (QNAR) modeling as a tool for predicting biological effects of manufactured nanoparticles | Q37833114 | ||
QSAR modeling of nanomaterials | Q37850776 | ||
Advancing risk assessment of engineered nanomaterials: application of computational approaches | Q38015999 | ||
Engineered nanomaterial risk. Lessons learnt from completed nanotoxicology studies: potential solutions to current and future challenges | Q38057778 | ||
Applying quantitative structure-activity relationship approaches to nanotoxicology: current status and future potential. | Q38060983 | ||
Development of structure-activity relationship for metal oxide nanoparticles | Q39149585 | ||
QSAR as a random event: modeling of nanoparticles uptake in PaCa2 cancer cells | Q39169538 | ||
Modeling biological activities of nanoparticles | Q39265233 | ||
A Large-Scale Empirical Evaluation of Cross-Validation and External Test Set Validation in (Q)SAR. | Q39410504 | ||
NIST gold nanoparticle reference materials do not induce oxidative DNA damage | Q39449119 | ||
Improving the interferences of methyl thiazolyl tetrazolium and IL-8 assays in assessing the cytotoxicity of nanoparticles. | Q39504418 | ||
Characterization of nanomaterial dispersion in solution prior to in vitro exposure using dynamic light scattering technique | Q40079958 | ||
Use of category approaches, read-across and (Q)SAR: general considerations | Q42603448 | ||
Mechanism-based categorization of aromatase inhibitors: a potential discovery and screening tool | Q43213435 | ||
Using nano-QSAR to predict the cytotoxicity of metal oxide nanoparticles | Q43415971 | ||
Broaden the discussion | Q43567960 | ||
Daphnia and fish toxicity of (benzo)triazoles: validated QSAR models, and interspecies quantitative activity-activity modelling. | Q43980660 | ||
Nano-SAR development for bioactivity of nanoparticles with considerations of decision boundaries | Q44932161 | ||
Developing descriptors to predict mechanical properties of nanotubes. | Q46061412 | ||
Exploring an ecotoxicity database with the OECD (Q)SAR Toolbox and DRAGON descriptors in order to prioritise testing on algae, daphnids, and fish. | Q46259209 | ||
We need answers | Q46666947 | ||
P433 | issue | 5 | |
P921 | main subject | nanotechnology | Q11468 |
P304 | page(s) | 636-642 | |
P577 | publication date | 2014-09-11 | |
P1433 | published in | Nanotoxicology | Q1964708 |
P1476 | title | Nano(Q)SAR: Challenges, pitfalls and perspectives | |
P478 | volume | 9 |
Q38665194 | A comprehensive framework for evaluating the environmental health and safety implications of engineered nanomaterials |
Q122638212 | A data reusability assessment in the nanosafety domain based on the NSDRA framework followed by an exploratory quantitative structure activity relationships (QSAR) modeling targeting cellular viability |
Q31106864 | A review of the applications of data mining and machine learning for the prediction of biomedical properties of nanoparticles |
Q47410600 | Aligning nanotoxicology with the 3Rs: What is needed to realise the short, medium and long-term opportunities? |
Q39702398 | Alternative Testing Strategies for Nanomaterials: State of the Science and Considerations for Risk Analysis |
Q38855212 | Approaches to Develop Alternative Testing Strategies to Inform Human Health Risk Assessment of Nanomaterials. |
Q28068302 | Aquatic Ecotoxicity Testing of Nanoparticles-The Quest To Disclose Nanoparticle Effects |
Q62730245 | Best Practices for QSAR Model Reporting: Physical and Chemical Properties, Ecotoxicity, Environmental Fate, Human Health, and Toxicokinetics Endpoints |
Q30885444 | Current situation on the availability of nanostructure-biological activity data |
Q52407388 | Dissolution and bandgap paradigms for predicting the toxicity of metal oxide nanoparticles in the marine environment: an in vivo study with oyster embryos. |
Q46359093 | Evaluating the toxicity of TiO2-based nanoparticles to Chinese hamster ovary cells and Escherichia coli: a complementary experimental and computational approach |
Q38729602 | Experimental-Computational Study of Carbon Nanotube Effects on Mitochondrial Respiration: In Silico Nano-QSPR Machine Learning Models Based on New Raman Spectra Transform with Markov-Shannon Entropy Invariants. |
Q45943650 | How the toxicity of nanomaterials towards different species could be simultaneously evaluated: a novel multi-nano-read-across approach. |
Q29248772 | Metal Oxide Nanomaterial QNAR Models: Available Structural Descriptors and Understanding of Toxicity Mechanisms |
Q47268851 | Modeling of Interactions between the Zebrafish Hatching Enzyme ZHE1 and A Series of Metal Oxide Nanoparticles: Nano-QSAR and Causal Analysis of Inactivation Mechanisms |
Q90746742 | NanoSolveIT Project: Driving nanoinformatics research to develop innovative and integrated tools for in silico nanosafety assessment |
Q47639565 | Perspectives from the NanoSafety Modelling Cluster on the validation criteria for (Q)SAR models used in nanotechnology |
Q36403512 | The MARINA Risk Assessment Strategy: A Flexible Strategy for Efficient Information Collection and Risk Assessment of Nanomaterials |
Q52584642 | Towards the Development of Global Nano-Quantitative Structure-Property Relationship Models: Zeta Potentials of Metal Oxide Nanoparticles. |
Q38647621 | Unmet needs: Research helps regulators do their jobs |
Q39183378 | What can nanosafety learn from drug development? The feasibility of "safety by design". |
Q36399257 | What if the number of nanotoxicity data is too small for developing predictive Nano-QSAR models? An alternative read-across based approach for filling data gaps |
Search more.