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Genetic Programming for the Induction of Decision Trees to Model Ecotoxicity Data
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Application of neural networks in structure-activity relationships
inferredcites →https://doi.org/10.1002/etc.5620190225openalex-snapshot
MULTIPLE COMPUTER-AUTOMATED STRUCTURE EVALUATION STUDY OF AQUATIC TOXICITY II. FATHEAD MINNOW
inferredcites →https://doi.org/10.1002/qsar.200390007openalex-snapshot
The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models
inferredcites →https://doi.org/10.1006/ijhc.1979.0308openalex-snapshot
Knowledge acquisition by encoding expert rules versus computer induction from examples: a case study involving soybean pathology
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Intelligent Hybrid Systems: Fuzzy Logic, Neural Networks, and Genetic Algorithms
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Toward an Alternative Comparison between Different Genetic Programming Systems
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The use of discriminant analysis, logistic regression and classification tree analysis in the development of classification models for human health effects
inferredcites →https://doi.org/10.1016/s0300-483x(97)03631-7openalex-snapshot
Computational predictive programs (expert systems) in toxicology
inferredcites →https://doi.org/10.1016/s1383-5742(97)00021-5openalex-snapshot
The first US National Toxicology Program exercise on the prediction of rodent carcinogenicity: definitive results
inferredcites →https://doi.org/10.1017/s026988899700101xopenalex-snapshot
A review of machine learning
inferredcites →https://doi.org/10.1021/ci020058sopenalex-snapshot
Decision Forest: Combining the Predictions of Multiple Independent Decision Tree Models
inferredcites →https://doi.org/10.1021/ci034188sopenalex-snapshot
Induction of Decision Trees via Evolutionary Programming
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/ci990116iopenalex-snapshot
Symbolic, Neural, and Bayesian Machine Learning Models for Predicting Carcinogenicity of Chemical Compounds
inferredcites →https://doi.org/10.1021/ci9903049openalex-snapshot
Analysis of a Large Structure/Biological Activity Data Set Using Recursive Partitioning
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
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Artificial intelligence approach to structure-activity studies. Computer automated structure evaluation of biological activity of organic molecules
inferredcites →https://doi.org/10.1021/ja00336a005openalex-snapshot
Orbital interactions in some polycycloalkyl halides: a photoelectron spectroscopic study
inferredcites →https://doi.org/10.1021/jm980697nopenalex-snapshot
Robust QSAR Models Using Bayesian Regularized Neural Networks
inferredcites →https://doi.org/10.1023/a:1009744630224openalex-snapshot
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
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Statistical Themes and Lessons for Data Mining
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Comparison of the computer programs DEREK and TOPKAT to predict bacterial mutagenicity
inferredcites →https://doi.org/10.1201/9781315139470openalex-snapshot
Classification And Regression Trees
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-1-61779-421-6_1openalex-snapshot
The Application of Structure–Activity Relationships to the Prediction of the Mutagenic Activity of Chemicals
inferred← citeshttps://doi.org/10.1007/978-3-319-47754-1_5openalex-snapshot
Literature Review of (Q)SAR Modelling of Nanomaterial Toxicity
inferred← citeshttps://doi.org/10.1007/978-3-540-36266-1_10openalex-snapshot
Advances in the Application of Machine Learning Techniques in Drug Discovery, Design and Development
inferred← citeshttps://doi.org/10.1007/s10910-018-0855-zopenalex-snapshot
An ensemble approach for in silico prediction of Ames mutagenicity
inferred← citeshttps://doi.org/10.1016/j.chemolab.2015.06.016openalex-snapshot
Chemometrics tools in QSAR/QSPR studies: A historical perspective
inferred← citeshttps://doi.org/10.1016/j.chemolab.2019.103826openalex-snapshot
Chemometrics approach for the prediction of chemical compounds’ toxicity degree based on quantum inspired optimization with applications in drug discovery
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.compchemeng.2010.04.018openalex-snapshot
Data flow modeling, data mining and QSAR in high-throughput discovery of functional nanomaterials
inferred← citeshttps://doi.org/10.1016/j.partic.2014.12.001openalex-snapshot
(Q)SAR modelling of nanomaterial toxicity: A critical review
inferred← citeshttps://doi.org/10.1016/j.swevo.2021.101006openalex-snapshot
Induction of decision trees as classification models through metaheuristics
inferred← citeshttps://doi.org/10.1016/s1570-7946(08)80102-2openalex-snapshot
Inductive data mining: Automatic generation of decision trees from data for QSAR modelling and process historical data analysis
inferred← citeshttps://doi.org/10.1021/ci100429uopenalex-snapshot
A Robust Boosting Regression Tree with Applications in Quantitative Structure−Activity Relationship Studies of Organic Compounds
inferred← citeshttps://doi.org/10.1021/ci7003253openalex-snapshot
Naïve Bayes Classification Using 2D Pharmacophore Feature Triplet Vectors
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Modified Particle Swarm Optimization Algorithm for Adaptively Configuring Globally Optimal Classification and Regression Trees
inferred← citeshttps://doi.org/10.1080/10629360600933723openalex-snapshot
Induction of decision trees using genetic programming for modelling ecotoxicity data: adaptive discretization of real-valued endpoints
inferred← citeshttps://doi.org/10.1080/10659360600562202openalex-snapshot
QSAR approach for mixture toxicity prediction using independent latent descriptors and fuzzy membership functions†
inferred← citeshttps://doi.org/10.1111/exsy.12415openalex-snapshot
Quantum‐inspired genetic programming model with application to predict toxicity degree for chemical compounds
inferred← citeshttps://doi.org/10.29099/ijair.v2i2.64openalex-snapshot
Quantum Inspired Genetic Programming Model to Predict Toxicity Degree for Chemical Compounds
inferred← citeshttps://doi.org/10.3109/17435390.2016.1161857openalex-snapshot
Accurate and interpretable nanoSAR models from genetic programming-based decision tree construction approaches
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Conceptual view approach of Machine Learning Based Recommendation System
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