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Combining Unsupervised and Supervised Artificial Neural Networks to PredictAquatic Toxicity
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Multivariate Discrimination between Modes of Toxic Action of Phenols
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/10.1002/qsar.200390014openalex-snapshot
Predicting Toxicity against the fathead Minnow by Adaptive Fuzzy Partition
inferredcites →https://doi.org/10.1007/3-540-45428-4_29openalex-snapshot
Combining Classifiers of Pesticides Toxicity through a Neuro-fuzzy Approach
inferredcites →https://doi.org/10.1007/3-540-48219-9_13openalex-snapshot
Mixing a Symbolic and a Subsymbolic Expert to Improve Carcinogenicity Prediction of Aromatic Compounds
inferredcites →https://doi.org/10.1007/978-1-4471-0793-4openalex-snapshot
Combining Artificial Neural Nets
inferredcites →https://doi.org/10.1007/978-3-642-88163-3openalex-snapshot
Self-Organization and Associative Memory
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Self-Organization and Associative Memory
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Assessing the risk assessment paradigm
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On the approximate realization of continuous mappings by neural networks
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Generation of QSAR sets with a self-organizing map
inferredcites →https://doi.org/10.1016/s0045-6535(02)00172-8openalex-snapshot
QSAR modeling with the electrotopological state indices: predicting the toxicity of organic chemicals
inferredcites →https://doi.org/10.1016/s0045-6535(02)00508-8openalex-snapshot
Comparative assessment of methods to develop QSARs for the prediction of the toxicity of phenols to Tetrahymena pyriformis
inferredcites →https://doi.org/10.1016/s0045-6535(99)00553-6openalex-snapshot
Using probabilistic neural networks to model the toxicity of chemicals to the fathead minnow (Pimephales promelas): A study based on 865 compounds
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/ci000134wopenalex-snapshot
Interpretation of Quantitative Structure−Property and −Activity Relationships
inferredcites →https://doi.org/10.1021/ci010329jopenalex-snapshot
An Integrated SOM-Fuzzy ARTMAP Neural System for the Evaluation of Toxicity
inferredcites →https://doi.org/10.1021/ci9903096openalex-snapshot
Predictive Carcinogenicity: A Model for Aromatic Compounds, with Nitrogen-Containing Substituents, Based on Molecular Descriptors Using an Artificial Neural Network
inferredcites →https://doi.org/10.1021/tx980273wopenalex-snapshot
Prediction of Fathead Minnow Acute Toxicity of Organic Compounds from Molecular Structure
inferredcites →https://doi.org/10.1080/1062936032000101501openalex-snapshot
Partial Least Squares Modelling of the Acute Toxicity of Aliphatic Compounds to Tetrahymena pyriformis
inferredcites →https://doi.org/10.1080/10629369908039170openalex-snapshot
Prediction of Acute Mammalian Toxicity of Organophosphorus Pesticide Compounds from Molecular Structure
inferredcites →https://doi.org/10.1142/s0218001404003423openalex-snapshot
MULTICLASS CLASSIFIER FROM A COMBINATION OF LOCAL EXPERTS: TOWARD DISTRIBUTED COMPUTATION FOR REAL-PROBLEM CLASSIFIERS
inferredcites →https://doi.org/10.7551/mitpress/5236.001.0001openalex-snapshot
Parallel Distributed Processing
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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In SilicoPlatforms for Predictive Ecotoxicology
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Machine Learning Approaches in Computational Toxicology Studies
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Counter‐Propagation Neural Networks for Modeling and Read Across in Aquatic (Fish) Toxicity
inferred← citeshttps://doi.org/10.1002/cjoc.200790138openalex-snapshot
Quantitative Structure Property Relations (QSPR) for Predicting Molar Diamagnetic Susceptibilities, χm, of Inorganic Compounds
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In silico Prediction of Aqueous Solubility: a Comparative Study of Local and Global Predictive Models
inferred← citeshttps://doi.org/10.1002/qsar.200630030openalex-snapshot
QSAR Studies Related to Toxicity of Aromatic Compounds on Tetrahymena Pyriformis
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Drug Design with Artificial Neural Networks
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Machine Learning and Deep Learning Methods in Ecotoxicological QSAR Modeling
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Machine Learning-Based Modeling of Drug Toxicity
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One-class approach: models for virtual screening of non-nucleoside HIV-1 reverse transcriptase inhibitors based on the concept of continuous molecular fields
inferred← citeshttps://doi.org/10.1016/b978-0-443-36474-7.00006-5openalex-snapshot
Application of artificial intelligence/machine learning in modeling chemical toxicity and ecotoxicity
inferred← citeshttps://doi.org/10.1016/b978-044452710-3/50009-4openalex-snapshot
Results of DEMETRA models
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Predicting acute aquatic toxicity of structurally diverse chemicals in fish using artificial intelligence approaches
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Application of QSPR-MLR methodology to solvatochromic behavior of quinoline in binary solvent HBD/DMF mixtures
inferred← citeshttps://doi.org/10.1016/j.theochem.2007.12.037openalex-snapshot
Structure–toxicity relationship for aliphatic compounds using quantum topological descriptors
inferred← citeshttps://doi.org/10.1021/acs.jcim.1c01079openalex-snapshot
Prior Knowledge for Predictive Modeling: The Case of Acute Aquatic Toxicity
inferred← citeshttps://doi.org/10.1021/ci050358kopenalex-snapshot
Toxicity-Indicating Structural Patterns
inferred← citeshttps://doi.org/10.1021/ci700231copenalex-snapshot
Modeling the Toxicity of Chemicals toTetrahymenapyriformisUsing Heuristic Multilinear Regression and Heuristic Back-Propagation Neural Networks
inferred← citeshttps://doi.org/10.1021/ci800164uopenalex-snapshot
Practical Outcomes of Applying Ensemble Machine Learning Classifiers to High-Throughput Screening (HTS) Data Analysis and Screening
inferred← citeshttps://doi.org/10.1021/tx049665vopenalex-snapshot
An Automated Group Contribution Method in Predicting Aquatic Toxicity: The Diatomic Fragment Approach
inferred← citeshttps://doi.org/10.1021/tx0600550openalex-snapshot
Classification of a Diverse Set of Tetrahymena pyriformis Toxicity Chemical Compounds from Molecular Descriptors by Statistical Learning Methods
inferred← citeshttps://doi.org/10.1039/c4tx00174eopenalex-snapshot
In silico prediction of chemical aquatic toxicity with chemical category approaches and substructural alerts
inferred← citeshttps://doi.org/10.1080/00319100601089679openalex-snapshot
Prediction dielectric constant of different ternary liquid mixtures at various temperatures and compositions using artificial neural networks
inferred← citeshttps://doi.org/10.1080/08839510802700847openalex-snapshot
ENSEMBLING REGRESSION MODELS TO IMPROVE THEIR PREDICTIVITY: A CASE STUDY IN QSAR (QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIPS) WITH COMPUTATIONAL CHEMOMETRICS
inferred← citeshttps://doi.org/10.1080/10629360701428854openalex-snapshot
Handling uncertainties in toxicity modelling using a fuzzy filter
inferred← citeshttps://doi.org/10.1080/1062936x.2010.502300openalex-snapshot
Using support vector regression coupled with the genetic algorithm for predicting acute toxicity to the fathead minnow
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.2015.1137353openalex-snapshot
QSAR study of the acute toxicity to fathead minnow based on a large dataset
inferred← citeshttps://doi.org/10.1080/15555270601009257openalex-snapshot
Spatial Transferability of PAH Data of the German ESB by Artificial Neural Networks
inferred← citeshttps://doi.org/10.1134/s1070363210120248openalex-snapshot
Computational methods and software in computer-aided combinatorial library design
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Insight into Quantum Computing and Deep Learning Approach for Drug Design
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HYBRID OF FUZZY CLUSTERING NEURAL NETWORK OVER NSL DATASET FOR INTRUSION DETECTION SYSTEM
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
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Fuzzy Filtering: A Mathematical Theory and Applications in Life Science
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