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Rule Extraction from a Mutagenicity Data Set Using Adaptively Grown Phylogenetic-like Trees
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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/em.2860060702openalex-snapshot
Reproducibility of microbial mutagenicity assays: I. Tests withSalmonella typhimurium andEscherichia coli using a standardized protocol
inferredcites →https://doi.org/10.1002/em.2860070902openalex-snapshot
Reproducibility of microbial mutagenicity assays: II. Testing of carcinogens and noncarcinogens inSalmonella typhimurium andEscherichia coli
inferredcites →https://doi.org/10.1002/em.2860080111openalex-snapshot
Sodium arsenite potentiates the clastogenicity and mutagenicity of dna crosslinking agents
inferredcites →https://doi.org/10.1002/em.2860080802openalex-snapshot
Salmonella mutagenicity tests: II. Results from the testing of 270 chemicals
inferredcites →https://doi.org/10.1002/em.2860090602openalex-snapshot
Salmonella Mutagenicity tests: III. Results from the testing of 255 chemicals
inferredcites →https://doi.org/10.1002/qsar.19920110208openalex-snapshot
MULTICASE 1. A Hierarchical Computer Automated Structure Evaluation Program
inferredcites →https://doi.org/10.1006/rtph.2000.1422openalex-snapshot
The Proportions of Mutagens among Chemicals in Commerce
inferredcites →https://doi.org/10.1016/0027-5107(94)90125-2openalex-snapshot
Use of SAR in computer-assited prediction of carcinogenicity and mutagenicity of chemicals by the TOPKAT program
inferredcites →https://doi.org/10.1016/0165-1110(89)90037-7openalex-snapshot
Structural basis of the mutagenicity of phenylazoaniline dyes
inferredcites →https://doi.org/10.1016/0165-1110(91)90002-dopenalex-snapshot
Classification according to chemical structure, mutagenicity to Salmonella and level of carcinogenicity of a further 39 chemicals tested for carcinogenicity by the U.S. National Toxicology Program
inferredcites →https://doi.org/10.1016/0165-1218(89)90037-2openalex-snapshot
Classification according to chemical structure, mutagenicity to Salmonella and level of carcinogenicity of a further 42 chemicals tested for carcinogenicity by the U.S. National Toxicology Program
inferredcites →https://doi.org/10.1016/s0027-5107(97)00202-9openalex-snapshot
Identification of `genotoxic' and `non-genotoxic' alerts for cancer in mice: the carcinogenic potency database
inferredcites →https://doi.org/10.1021/ci010244iopenalex-snapshot
Analysis of Large Screening Data Sets via Adaptively Grown Phylogenetic-Like Trees
inferredcites →https://doi.org/10.1021/ja00336a004openalex-snapshot
Artificial intelligence approach to structure-activity studies. Computer automated structure evaluation of biological activity of organic molecules
inferredcites →https://doi.org/10.1080/10629369908039182openalex-snapshot
Knowledge-Based Expert Systems for Toxicity and Metabolism Prediction: DEREK, StAR and METEOR
inferredcites →https://doi.org/10.1093/mutage/7.3.211openalex-snapshot
Structural basis of the genotoxicity of polycyclic aromatic hydrocarbons
inferredcites →https://doi.org/10.1177/096032719101000405openalex-snapshot
Computer Prediction of Possible Toxic Action from Chemical Structure; The DEREK System
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.200247210openalex-snapshot
Rule Extraction from a Mutagenicity Data Set Using Adaptively Grown Phylogenetic‐Like Trees.
inferred← citeshttps://doi.org/10.1007/s11030-007-9057-5openalex-snapshot
Prediction of mutagenic toxicity by combination of Recursive Partitioning and Support Vector Machines
inferred← citeshttps://doi.org/10.1016/j.compchemeng.2009.04.005openalex-snapshot
Inductive data mining based on genetic programming: Automatic generation of decision trees from data for process historical data analysis
inferred← citeshttps://doi.org/10.1016/j.mrgentox.2024.503785openalex-snapshot
Applications of intelligent technology in the evaluation of mutagenicity
inferred← citeshttps://doi.org/10.1021/ci034013iopenalex-snapshot
Predicting the Genotoxicity of Secondary and Aromatic Amines Using Data Subsetting To Generate a Model Ensemble
inferred← citeshttps://doi.org/10.1021/ci0503715openalex-snapshot
Substructure Mining Using Elaborate Chemical Representation
inferred← citeshttps://doi.org/10.1021/jm040835aopenalex-snapshot
Derivation and Validation of Toxicophores for Mutagenicity Prediction
inferred← citeshttps://doi.org/10.1021/jm049902ropenalex-snapshot
Deriving Knowledge through Data Mining High-Throughput Screening Data
inferred← citeshttps://doi.org/10.1021/tx020104iopenalex-snapshot
Predicting the Genotoxicity of Thiophene Derivatives from Molecular Structure
inferred← citeshttps://doi.org/10.1093/chemse/bji048openalex-snapshot
Building a Tree of Knowledge: Analysis of Bitter Molecules
inferred← citeshttps://doi.org/10.1093/mutage/geh043openalex-snapshot
Three new consensus QSAR models for the prediction of Ames genotoxicity
inferred← citeshttps://doi.org/10.1186/1758-2946-6-8openalex-snapshot
Feature combination networks for the interpretation of statistical machine learning models: application to Ames mutagenicity
inferred← citeshttps://doi.org/10.1201/9780203010822.ch5openalex-snapshot
Public Sources of Mutagenicity and Carcinogenicity Data
inferred← citeshttps://doi.org/10.2788/26123openalex-snapshot
Review of QSAR Models and Software Tools for Predicting of Genotoxicity and Carcinogenicity
inferred← citeshttps://doi.org/10.5281/zenodo.2567368openalex-snapshot
Molecular Substructure Mining Approaches for Computer-Aided Drug Discovery A Review
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