Accurate and interpretable nanoSAR models from genetic programming-based decision tree construction approaches

scientific article published on 08 March 2016

Accurate and interpretable nanoSAR models from genetic programming-based decision tree construction approaches is …
instance of (P31):
scholarly articleQ13442814

External links are
P5530Altmetric DOI10.3109/17435390.2016.1161857
P6179Dimensions Publication ID1025216782
P356DOI10.3109/17435390.2016.1161857
P8608Fatcat IDrelease_jwdzl2zwjzecnbq5jp74al5exm
P953full work available at URLhttp://eprints.whiterose.ac.uk/96571/
P6366Microsoft Academic ID2299433787
P698PubMed publication ID26956430
P1154Scopus EID2-s2.0-84962359284

P50authorDavid WinklerQ52234967
CaiYun MaQ53843828
P2093author name stringXue Z Wang
Ceyda Oksel
Terry Wilkins
Cai Y Ma
P2860cites workCausal inference methods to assist in mechanistic interpretation of classification nano-SAR modelsQ56999520
Nano-quantitative structure–activity relationship modeling using easily computable and interpretable descriptors for uptake of magnetofluorescent engineered nanoparticles in pancreatic cancer cellsQ57008918
Engineered nanomaterials for water treatment and remediation: Costs, benefits, and applicabilityQ58829894
An ISA-TAB-Nano based data collection framework to support data-driven modelling of nanotoxicologyQ27942681
Toxic potential of materials at the nanolevelQ28295314
Quantitative nanostructure-activity relationship modelingQ28385022
Use of metal oxide nanoparticle band gap to develop a predictive paradigm for oxidative stress and acute pulmonary inflammationQ28392757
Computer-aided design of carbon nanotubes with the desired bioactivity and safety profilesQ28829021
Principal component and causal analysis of structural and acute in vitro toxicity data for nanoparticlesQ30616632
Using experimental data of Escherichia coli to develop a QSAR model for predicting the photo-induced cytotoxicity of metal oxide nanoparticlesQ30722652
Current situation on the availability of nanostructure-biological activity dataQ30885444
Genetic programming for the induction of decision trees to model ecotoxicity data.Q30996702
Induction of decision trees using genetic programming for modelling ecotoxicity data: adaptive discretization of real-valued endpoints.Q33260813
Developing and validating predictive decision tree models from mining chemical structural fingerprints and high-throughput screening data in PubChemQ33371987
Classification NanoSAR development for cytotoxicity of metal oxide nanoparticlesQ33859712
Cell-specific targeting of nanoparticles by multivalent attachment of small moleculesQ34462201
The utility of structure-activity relationship (SAR) models for prediction and covariate selection in developmental toxicity: comparative analysis of logistic regression and decision tree models.Q35758119
Toward the development of "nano-QSARs": advances and challengesQ37605014
Computational methods to predict the reactivity of nanoparticles through structure-property relationshipsQ37702249
Exploring quantitative nanostructure-activity relationships (QNAR) modeling as a tool for predicting biological effects of manufactured nanoparticlesQ37833114
QSAR modeling of nanomaterialsQ37850776
Advancing risk assessment of engineered nanomaterials: application of computational approachesQ38015999
Applying quantitative structure-activity relationship approaches to nanotoxicology: current status and future potential.Q38060983
Impact of nanoparticles on human and environment: review of toxicity factors, exposures, control strategies, and future prospectsQ38303433
Difficulties in establishing regulations for engineered nanomaterials and considerations for policy makers: avoiding an unbalance between benefits and risks.Q38517151
Applications of nanoparticles in cancer medicine and beyond: optical and multimodal in vivo imaging, tissue targeting and drug deliveryQ38569765
Applications of Nanomaterials in Food PackagingQ38682785
Recent advances, and unresolved issues, in the application of computational modelling to the prediction of the biological effects of nanomaterialsQ38684997
Discovery and Optimization of Materials Using Evolutionary Approaches.Q38832763
Towards understanding mechanisms governing cytotoxicity of metal oxides nanoparticles: hints from nano-QSAR studiesQ38979118
Development of structure-activity relationship for metal oxide nanoparticlesQ39149585
Modeling biological activities of nanoparticlesQ39265233
Comparative study of predictive computational models for nanoparticle-induced cytotoxicityQ39691029
Prediction of nanoparticles-cell association based on corona proteins and physicochemical properties.Q40955960
Deep neural nets as a method for quantitative structure-activity relationshipsQ41506565
Using nano-QSAR to predict the cytotoxicity of metal oxide nanoparticlesQ43415971
Nano-SAR development for bioactivity of nanoparticles with considerations of decision boundariesQ44932161
A QSAR toxicity study of a series of alkaloids with the lycoctonine skeleton.Q46896685
Decision tree SAR models for developmental toxicity based on an FDA/TERIS databaseQ47912674
Surface chemistry of gold nanoparticles mediates their exocytosis in macrophages.Q53280836
A strategy for grouping of nanomaterials based on key physico-chemical descriptors as a basis for safer-by-design NMsQ55167377
P433issue7
P921main subjectdecision treeQ831366
P304page(s)1001-1012
P577publication date2016-03-08
P1433published inNanotoxicologyQ1964708
P1476titleAccurate and interpretable nanoSAR models from genetic programming-based decision tree construction approaches
P478volume10

Reverse relations

cites work (P2860)
Q64231472Computational models for the assessment of manufactured nanomaterials: Development of model reporting standards and mapping of the model landscape
Q47282095Decision tree models to classify nanomaterials according to the DF4nanoGrouping scheme
Q90499828Fermented mulberry leaf meal as fishmeal replacer in the formulation of feed for carp Labeo rohita and catfish Heteropneustes fossilis-optimization by mathematical programming
Q36199516Materials Informatics: Statistical Modeling in Material Science
Q92632878Practices and Trends of Machine Learning Application in Nanotoxicology
Q36399257What 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.