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Tuning Neural and Fuzzy-Neural Networks for Toxicity Modeling
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/etc.5620160514openalex-snapshot
Predicting modes of toxic action from chemical structure: Acute toxicity in the fathead minnow ( Pimephales promelas )
inferredcites →https://doi.org/10.1007/3-540-45428-4_29openalex-snapshot
Combining Classifiers of Pesticides Toxicity through a Neuro-fuzzy Approach
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Soft Computing, Molecular Orbital, and Density Functional Theory in the Design of Safe Chemicals
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Soft Computing and Density Functional Theory in the Design of Safe Textile Chemicals
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Prediction of environmental toxicity based on structure-activity relationships using mechanistic information
inferredcites →https://doi.org/10.1016/s0165-1218(96)90092-0openalex-snapshot
QSARS of mutagens and carcinogens: Two case studies illustrating problems in the construction of models for noncongeneric chemicals
inferredcites →https://doi.org/10.1016/s0300-483x(97)03631-7openalex-snapshot
Computational predictive programs (expert systems) in toxicology
inferredcites →https://doi.org/10.1021/ci025520nopenalex-snapshot
The Importance of Scaling in Data Mining for Toxicity Prediction
inferredcites →https://doi.org/10.1021/ci970231yopenalex-snapshot
A Study of Structure−Carcinogenic Potency Relationship with Artificial Neural Networks. The Using of Descriptors Related to Geometrical and Electronic Structures
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Predictive Carcinogenicity: A Model for Aromatic Compounds, with Nitrogen-Containing Substituents, Based on Molecular Descriptors Using an Artificial Neural Network
inferredcites →https://doi.org/10.1023/a:1016108730534openalex-snapshot
Neural and Neuro-Fuzzy Integration in a Knowledge-Based System for Air Quality Prediction
inferredcites →https://doi.org/10.1080/10629360108039836openalex-snapshot
Factors Influencing Predictive Models for Toxicology
inferredcites →https://doi.org/10.1109/34.730549openalex-snapshot
Are multilayer perceptrons adequate for pattern recognition and verification?
inferredcites →https://doi.org/10.1109/is.2002.1044269openalex-snapshot
Neural and neuro-fuzzy models of toxic action of phenols
inferredcites →https://doi.org/10.1162/neco.1991.3.4.617openalex-snapshot
Kolmogorov's Theorem Is Relevant
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/chin.200321207openalex-snapshot
Tuning Neutral and Fuzzy‐Neutral Networks for Toxicity Modeling.
inferred← citeshttps://doi.org/10.1002/qsar.200330864openalex-snapshot
QSTR with Extended Topochemical Atom Indices. 3. Toxicity of Nitrobenzenes to Tetrahymena pyriformis
inferred← citeshttps://doi.org/10.1002/qsar.200530172openalex-snapshot
QSTR with Extended Topochemical Atom Indices. 7. QSAR of Substituted Benzenes toSaccharomyces cerevisiae
inferred← citeshttps://doi.org/10.1007/978-1-4419-0197-2_1openalex-snapshot
Artificial Neural Network Modeling of the Environmental Fate and Ecotoxicity of Chemicals
inferred← citeshttps://doi.org/10.1007/978-1-60327-101-1_5openalex-snapshot
Artificial Neural Network Modeling in Environmental Toxicology
inferred← citeshttps://doi.org/10.1007/s00894-005-0033-7openalex-snapshot
QSTR with extended topochemical atom (ETA) indices. VI. Acute toxicity of benzene derivatives to tadpoles (Rana japonica)
inferred← citeshttps://doi.org/10.1007/s11356-014-3092-4openalex-snapshot
Improving nitrogen removal using a fuzzy neural network-based control system in the anoxic/oxic process
inferred← citeshttps://doi.org/10.1016/j.chemolab.2009.11.002openalex-snapshot
Prediction of the acute toxicity of chemical compounds to the fathead minnow by machine learning approaches
inferred← citeshttps://doi.org/10.1016/j.chemosphere.2013.06.002openalex-snapshot
Quantitative structure–activity relationship to predict acute fish toxicity of organic solvents
inferred← citeshttps://doi.org/10.1016/j.ecoenv.2013.05.017openalex-snapshot
Predicting acute aquatic toxicity of structurally diverse chemicals in fish using artificial intelligence approaches
inferred← citeshttps://doi.org/10.1016/j.etap.2016.11.010openalex-snapshot
Improved building up a model of toxicity towards Pimephales promelas by the Monte Carlo method
inferred← citeshttps://doi.org/10.1021/ac902636qopenalex-snapshot
Computational Neural Networks Driving Complex Analytical Problem Solving
inferred← citeshttps://doi.org/10.1021/cr9003105openalex-snapshot
Toward a Comprehensive Molecular Design Framework for Reduced Hazard
inferred← citeshttps://doi.org/10.1021/es801991copenalex-snapshot
Ecological Risk Assessment: From Book-Keeping to Chemical Stress Ecology
inferred← citeshttps://doi.org/10.1080/10629360701428854openalex-snapshot
Handling uncertainties in toxicity modelling using a fuzzy filter
inferred← citeshttps://doi.org/10.1080/1062936x.2015.1018938openalex-snapshot
A similarity-based QSAR model for predicting acute toxicity towards the fathead minnow (Pimephales promelas)
inferred← citeshttps://doi.org/10.1080/1062936x.2016.1196388openalex-snapshot
Robust modelling of acute toxicity towards fathead minnow (Pimephales promelas)using counter-propagation artificial neural networks and genetic algorithm
inferred← citeshttps://doi.org/10.1109/icccyb.2006.305708openalex-snapshot
A Data-Driven Approach for Improved Effective Classification in Predictive Toxicology
inferred← citeshttps://doi.org/10.1109/icsmc.2008.4811453openalex-snapshot
A Minimal Coverage-based Classification method and its application in predictive toxicology data mining
inferred← citeshttps://doi.org/10.1111/j.1742-7843.2006.pto_471.xopenalex-snapshot
The Challenge of Predicting Drug Toxicityin silico
inferred← citeshttps://doi.org/10.1142/s1469026805001635openalex-snapshot
FUZZY kNNMODEL APPLIED TO PREDICTIVE TOXICOLOGY DATA MINING
inferred← citeshttps://doi.org/10.1166/jtt.2014.1005openalex-snapshot
ToxiPred: A Server for Prediction of Aqueous Toxicity of Small Chemical Molecules in T. Pyriformis
inferred← citeshttps://doi.org/10.25673/4015openalex-snapshot
Data-driven system identification via evolutionary retrieval of Takagi-Sugeno fuzzy models
inferred← citeshttps://doi.org/10.3390/ijms25084156openalex-snapshot
Merging Counter-Propagation and Back-Propagation Algorithms: Overcoming the Limitations of Counter-Propagation Neural Network Models
inferred← citeshttps://doi.org/10.4322/tmm.00502007openalex-snapshot
APLICAÇÃO DE TÉCNICAS DE INTELIGÊNCIA COMPUTACIONAL PARA PREDIÇÃO DE PROPRIEDADES MECÂNICAS DE AÇOS DE ALTA RESISTÊNCIA MICROLIGADOS
inferred← citeshttps://doi.org/10.5012/bkcs.2012.33.2.613openalex-snapshot
Prediction of Acute Toxicity to Fathead Minnow by Local Model Based QSAR and Global QSAR Approaches
inferred← citeshttps://doi.org/10.5772/7222openalex-snapshot
Fuzzy Filtering: A Mathematical Theory and Applications in Life Science
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