On the public citation graph
Modeling Toxicity by Using Supervised Kohonen Neural Networks
0 signed · 58 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/9783527613106openalex-snapshot
Handbook of Molecular Descriptors
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/10.1016/s0003-2670(98)00782-xopenalex-snapshot
Study of structure–toxicity relationship by a counterpropagation neural network
inferredcites →https://doi.org/10.1016/s0169-7439(01)00142-3openalex-snapshot
Comparison of spectrum-like representation of 3D chemical structure with other representations when used for modelling biological activity
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
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/ci990437uopenalex-snapshot
Molecular Dataset Diversity Indices and Their Applications to Comparison of Chemical Databases and QSAR Analysis
inferredcites →https://doi.org/10.1135/cccc19991551openalex-snapshot
QSPR and QSAR Models Derived Using Large Molecular Descriptor Spaces. A Review of CODESSA Applications
inferredcites →https://doi.org/10.5860/choice.28-3323openalex-snapshot
Neural network architectures: an introduction
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/9781119681397.ch8openalex-snapshot
Counter‐Propagation Neural Networks for Modeling and Read Across in Aquatic (Fish) Toxicity
inferred← citeshttps://doi.org/10.1002/jcc.21351openalex-snapshot
A new strategy of outlier detection for QSAR/QSPR
inferred← citeshttps://doi.org/10.1002/minf.201000100openalex-snapshot
Chemoinformatics as a Theoretical Chemistry Discipline
inferred← citeshttps://doi.org/10.1007/978-1-4939-7899-1_5openalex-snapshot
Machine Learning Methods in Computational Toxicology
inferred← citeshttps://doi.org/10.1007/978-1-60327-101-1_5openalex-snapshot
Artificial Neural Network Modeling in Environmental Toxicology
inferred← citeshttps://doi.org/10.1007/978-3-031-78728-7_6openalex-snapshot
Applications of Predictive Modeling for Fullerenes
inferred← citeshttps://doi.org/10.1007/978-3-319-56850-8_14openalex-snapshot
Counter-Propagation Artificial Neural Network Models for Prediction of Carcinogenicity of Non-congeneric Chemicals for Regulatory Uses
inferred← citeshttps://doi.org/10.1007/978-3-540-69968-2_6openalex-snapshot
Grid Computing for the Estimation of Toxicity: Acute Toxicity on Fathead Minnow (Pimephales promelas)
inferred← citeshttps://doi.org/10.1007/s00894-016-3156-0openalex-snapshot
Acute aquatic toxicity of organic solvents modeled by QSARs
inferred← citeshttps://doi.org/10.1007/s10822-008-9192-9openalex-snapshot
Utilizing high throughput screening data for predictive toxicology models: protocols and application to MLSCN assays
inferred← citeshttps://doi.org/10.1007/s11426-007-0056-2openalex-snapshot
Constructing virtual combinatorial fragment libraries based upon MDL Drug Data Report database
inferred← citeshttps://doi.org/10.1016/j.aca.2009.01.041openalex-snapshot
Automatic adjustment of the relative importance of different input variables for optimization of counter-propagation artificial neural networks
inferred← citeshttps://doi.org/10.1016/j.chemolab.2003.09.005openalex-snapshot
Similarity study of proteomic maps
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.csbj.2022.02.006openalex-snapshot
How fullerene derivatives (FDs) act on therapeutically important targets associated with diabetic diseases
inferred← citeshttps://doi.org/10.1016/j.jhazmat.2020.123724openalex-snapshot
Quantitative structure-toxicity relationship model for acute toxicity of organophosphates via multiple administration routes in rats and mice
inferred← citeshttps://doi.org/10.1016/j.sab.2005.03.005openalex-snapshot
Classification of Antarctic algae by applying Kohonen neural network with 14 elements determined by inductively coupled plasma optical emission spectrometry
inferred← citeshttps://doi.org/10.1016/s0922-3487(03)23008-2openalex-snapshot
Artificial neural networks in molecular structures—property studies
inferred← citeshttps://doi.org/10.1016/s1741-8364(04)02402-3openalex-snapshot
Using artificial neural networks to drive virtual screening of combinatorial libraries
inferred← citeshttps://doi.org/10.1021/jm701318xopenalex-snapshot
Neural Networks as Valuable Tools To Differentiate between Sesquiterpene Lactones’ Inhibitory Activity on Serotonin Release and on NF-κB
inferred← citeshttps://doi.org/10.1023/a:1026132402754openalex-snapshot
ANVAS: Artificial Neural Variables Adaptation System for descriptor selection
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/10.1080/10629360903563250openalex-snapshot
Counter propagation artificial neural network categorical models for prediction of carcinogenicity for non-congeneric chemicals
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.2014.919959openalex-snapshot
Structure–activity relationship study of trifluoromethylketone inhibitors of insect juvenile hormone esterase: Comparison of several classification methods
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/10.1080/10659360600787650openalex-snapshot
Validation of counter propagation neural network models for predictive toxicology according to the OECD principles: a case study
inferred← citeshttps://doi.org/10.1080/17435390.2017.1310949openalex-snapshot
The way to cover prediction for cytotoxicity for all existing nano-sized metal oxides by using neural network method
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/10.1111/j.1747-0285.2010.00966.xopenalex-snapshot
Artificial Neural Networks and the Study of the Psychoactivity of Cannabinoid Compounds
inferred← citeshttps://doi.org/10.1177/026119290603400607openalex-snapshot
Integrated Decision-tree Testing Strategies for Environmental Toxicity with Respect to the Requirements of the EU REACH Legislation
inferred← citeshttps://doi.org/10.1177/026119290803601s04openalex-snapshot
Integrated Decision-tree Testing Strategies for Environmental Toxicity with Respect to the Requirements of the EU REACH Legislation
inferred← citeshttps://doi.org/10.1186/1752-153x-4-s1-s3openalex-snapshot
New public QSAR model for carcinogenicity
inferred← citeshttps://doi.org/10.1205/psep.04389openalex-snapshot
Using an Ensemble of Neural Based QSARs for the Prediction of Toxicological Properties of Chemical Contaminants
inferred← citeshttps://doi.org/10.1289/ehp14001openalex-snapshot
Unlocking the Potential of Clustering and Classification Approaches: Navigating Supervised and Unsupervised Chemical Similarity
inferred← citeshttps://doi.org/10.1517/17460441.2010.524924openalex-snapshot
Exploiting PubChem for virtual screening
inferred← citeshttps://doi.org/10.2174/092986712802884259openalex-snapshot
Machine Learning Techniques and Drug Design
inferred← citeshttps://doi.org/10.24963/ijcai.2022/269openalex-snapshot
Non-Euclidean Self-Organizing Maps
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/ijms241814160openalex-snapshot
Cheminformatics and Machine Learning Approaches to Assess Aquatic Toxicity Profiles of Fullerene Derivatives
inferred← citeshttps://doi.org/10.3390/nano10010090openalex-snapshot
A Comprehensive Cheminformatics Analysis of Structural Features Affecting the Binding Activity of Fullerene Derivatives
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
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.