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Symbolic, Neural, and Bayesian Machine Learning Models for Predicting Carcinogenicity of Chemical Compounds
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Comparison between Rodent Carcinogenicity Test Results of 44 Chemicals and a Number of Predictive Systems
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Fusion and propagation with multiple observations in belief networks
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Simplifying decision trees
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Predictive Carcinogenicity: A Model for Aromatic Compounds, with Nitrogen-Containing Substituents, Based on Molecular Descriptors Using an Artificial Neural Network
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Knowledge-Based Learning in Exploratory Science: Learning Rules to Predict Rodent Carcinogenicity
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Long-Term Chemical Carcinogenesis Experiments for Identifying Potential Human Cancer Hazards: Collective Database of the National Cancer Institute and National Toxicology Program (1976-1991)
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Fraud/Uncollectible Debt Detection Using a Bayesian Network Based Learning System: A Rare Binary Outcome with Mixed Data Structures
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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ChemInform Abstract: Symbolic, Neural, and Bayesian Machine Learning Models for Predicting Carcinogenicity of Chemical Compounds.
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What's Left for a Computational Chemist To Do in the Age of Machine Learning?
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Neural networks for effect prediction in environmental and health issues using large datasets
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Lazy Learning for Predictive Toxicology based on a Chemical Ontology
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Comparative Study of Classification Algorithms Using Molecular Descriptors in Toxicological DataBases
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Prediction of carcinogenicity for diverse chemicals based on substructure grouping and SVM modeling
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Artificial neural networks and genetic algorithms in QSAR
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Machine Learning and Deep Learning in Chemical Health and Safety: A Systematic Review of Techniques and Applications
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Genetic Programming for the Induction of Decision Trees to Model Ecotoxicity Data
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A Comparative Study on Feature Selection Methods for Drug Discovery
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Local and Global Quantitative Structure−Activity Relationship Modeling and Prediction for the Baseline Toxicity
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Probabilistic Approaches in Activity Prediction
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Hybrid intelligent systems for predictive toxicology - a distributed approach
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Development of the Chemical Safety Database System CAESAR
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Decision tree models for data mining in hit discovery
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Yeast Gene Function Prediction from Different Data Sources: An Empirical Comparison
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Prediction of Carcinogenicity of Chlorine-containing Organic Compounds by Neural Network.
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Toxicity Information and Its QSAR Prediction of Chemical Substances
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Usage of Si, P, Se, and Ca Decrease Arsenic Concentration/Toxicity in Rice, a Review
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An Approach to the Classification of Cutting Vibration on Machine Tools
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Neural-Symbolic Processing in Business Applications
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