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Prediction of Genotoxicity of Chemical Compounds by Statistical Learning Methods

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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/(sici)1098-2280(1998)31:1<1::aid-em1>3.0.co;2-ropenalex-snapshot
Genetic toxicology: Impact on the next generation of toxicology
inferredcites →https://doi.org/10.1002/1521-3838(200110)20:3<227::aid-qsar227>3.0.co;2-yopenalex-snapshot
Use of Support Vector Machine in Pattern Classification: Application to QSAR Studies
inferredcites →https://doi.org/10.1002/9783527613106openalex-snapshot
Handbook of Molecular Descriptors
inferredcites →https://doi.org/10.1002/em.20013openalex-snapshot
Assessment of the sensitivity of the computational programs DEREK, TOPKAT, and MCASE in the prediction of the genotoxicity of pharmaceutical molecules
inferredcites →https://doi.org/10.1002/em.2860070613openalex-snapshot
Fundamental structural alerts to potential carcinogenicity or noncarcinogenicity
inferredcites →https://doi.org/10.1002/qsar.200310006openalex-snapshot
Support Vector Machines for ADME Property Classification
inferredcites →https://doi.org/10.1007/978-1-4757-2440-0openalex-snapshot
The Nature of Statistical Learning Theory
inferredcites →https://doi.org/10.1007/bf02869528openalex-snapshot
Estimation of a multivariate density
inferredcites →https://doi.org/10.1016/0005-2795(75)90109-9openalex-snapshot
Comparison of the predicted and observed secondary structure of T4 phage lysozyme
inferredcites →https://doi.org/10.1016/0165-1110(93)90019-jopenalex-snapshot
The SOS chromotest: a review
inferredcites →https://doi.org/10.1016/0893-6080(90)90049-qopenalex-snapshot
Probabilistic neural networks
inferredcites →https://doi.org/10.1016/s0097-8485(01)00094-8openalex-snapshot
Drug design by machine learning: support vector machines for pharmaceutical data analysis
inferredcites →https://doi.org/10.1016/s0169-409x(02)00012-1openalex-snapshot
Computer systems for the prediction of toxicity: an update
inferredcites →https://doi.org/10.1016/s0893-6080(03)00103-5openalex-snapshot
An accelerated procedure for recursive feature ranking on microarray data
inferredcites →https://doi.org/10.1016/s0923-2508(02)01342-6openalex-snapshot
SOS Chromotest methodology for fundamental genetic research
inferredcites →https://doi.org/10.1016/s0928-0987(00)00162-7openalex-snapshot
VolSurf: a new tool for the pharmacokinetic optimization of lead compounds
inferredcites →https://doi.org/10.1016/s1359-6446(00)01559-2openalex-snapshot
Predicting human safety: screening and computational approaches
inferredcites →https://doi.org/10.1016/s1359-6446(97)01099-4openalex-snapshot
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inferredcites →https://doi.org/10.1016/s1383-5718(00)00167-4openalex-snapshot
Prediction of the genotoxicity of aromatic and heteroaromatic amines using electrotopological state indices
inferredcites →https://doi.org/10.1016/s1383-5742(01)00055-2openalex-snapshot
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inferredcites →https://doi.org/10.1016/s1383-5742(02)00044-3openalex-snapshot
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inferredcites →https://doi.org/10.1016/s1383-5742(97)00019-7openalex-snapshot
Review of mutagenicity of monocyclic aromatic amines: quantitative structure–activity relationships
inferredcites →https://doi.org/10.1017/cbo9780511801389openalex-snapshot
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inferredcites →https://doi.org/10.1021/ci00016a005openalex-snapshot
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inferredcites →https://doi.org/10.1021/ci034013iopenalex-snapshot
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inferredcites →https://doi.org/10.1021/ci034233wopenalex-snapshot
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inferredcites →https://doi.org/10.1021/ci049869hopenalex-snapshot
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inferredcites →https://doi.org/10.1021/ci9800716openalex-snapshot
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inferredcites →https://doi.org/10.1021/ci980109eopenalex-snapshot
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inferredcites →https://doi.org/10.1021/ci980411nopenalex-snapshot
Prediction of the Brain−Blood Distribution of a Large Set of Drugs from Structurally Derived Descriptors Using Partial Least-Squares (PLS) Modeling
inferredcites →https://doi.org/10.1021/cr950202ropenalex-snapshot
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inferredcites →https://doi.org/10.1021/ja00299a024openalex-snapshot
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inferredcites →https://doi.org/10.1021/jm990968+openalex-snapshot
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inferredcites →https://doi.org/10.1021/tx020077wopenalex-snapshot
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inferredcites →https://doi.org/10.1021/tx020104iopenalex-snapshot
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inferredcites →https://doi.org/10.1021/tx030032aopenalex-snapshot
Predicting the Genotoxicity of Polycyclic Aromatic Compounds from Molecular Structure with Different Classifiers
inferredcites →https://doi.org/10.1023/a:1009715923555openalex-snapshot
A Tutorial on Support Vector Machines for Pattern Recognition
inferredcites →https://doi.org/10.1023/a:1012450327387openalex-snapshot
Choosing Multiple Parameters for Support Vector Machines
inferredcites →https://doi.org/10.1023/a:1012487302797openalex-snapshot
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inferredcites →https://doi.org/10.1038/nrd1032openalex-snapshot
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inferredcites →https://doi.org/10.1073/pnas.97.1.262openalex-snapshot
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inferredcites →https://doi.org/10.1080/00401706.1983.10487906openalex-snapshot
Applied Multivariate Statistical Analysis
inferredcites →https://doi.org/10.1080/01431160110040323openalex-snapshot
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inferredcites →https://doi.org/10.1089/10665270260518317openalex-snapshot
Predicting CNS Permeability of Drug Molecules: Comparison of Neural Network and Support Vector Machine Algorithms
inferredcites →https://doi.org/10.1093/bioinformatics/16.10.906openalex-snapshot
Support vector machine classification and validation of cancer tissue samples using microarray expression data
inferredcites →https://doi.org/10.1093/bioinformatics/16.5.412openalex-snapshot
Assessing the accuracy of prediction algorithms for classification: an overview
inferredcites →https://doi.org/10.1093/bioinformatics/17.5.455openalex-snapshot
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inferredcites →https://doi.org/10.1093/bioinformatics/18.suppl_2.s75openalex-snapshot
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inferredcites →https://doi.org/10.1093/toxsci/kfh082openalex-snapshot
Prediction of Torsade-Causing Potential of Drugs by Support Vector Machine Approach No funding was used to assist in conducting the study and the authors do not have any conflicts of interest directly relevant to the contents of the manuscript.
inferredcites →https://doi.org/10.1109/csb.2003.1227321openalex-snapshot
Discovering compact and highly discriminative features or combinations of drug activities using support vector machines
inferredcites →https://doi.org/10.1111/j.2042-7158.1998.tb06879.xopenalex-snapshot
Genetic Toxicology
inferredcites →https://doi.org/10.1177/002029400103400803openalex-snapshot
Support Vector Machines in Combinatorial Chemistry
inferredcites →https://doi.org/10.1186/1471-2105-5-64openalex-snapshot
Feature selection for splice site prediction: A new method using EDA-based feature ranking
inferredcites →https://doi.org/10.1214/aoms/1177704472openalex-snapshot
On Estimation of a Probability Density Function and Mode
inferredcites →https://doi.org/10.1289/ehp.96104s51065openalex-snapshot
Prediction of rodent carcinogenicity using the DEREK system for 30 chemicals currently being tested by the National Toxicology Program. The DEREK Collaborative Group.
inferredcites →https://doi.org/10.1385/mb:20:2:153openalex-snapshot
Screening with Tumor Markers: Critical Issues
inferredcites →https://doi.org/10.1518/hfes.45.3.408.27248openalex-snapshot
Comparing Statistical and Machine Learning Classifiers: Alternatives for Predictive Modeling in Human Factors Research
inferredcites →https://doi.org/10.2307/2531616openalex-snapshot
Applied Multivariate Statistical Analysis.
inferredcites →https://doi.org/10.2307/3433032openalex-snapshot
Prediction of Rodent Carcinogenicity Using the DEREK System for 30 Chemicals Currently Being Tested by the National Toxicology Program
inferredcites →https://doi.org/10.7551/mitpress/1120.001.0001openalex-snapshot
Advances in Neural Information Processing Systems 14

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inferred← citeshttps://doi.org/10.1002/9780470116449.ch6openalex-snapshot
Applications of Support Vector Machines in Chemistry
inferred← citeshttps://doi.org/10.1002/9780470567623.ch12openalex-snapshot
Data Mining in Pharmacovigilance
inferred← citeshttps://doi.org/10.1002/9781118783344.ch6openalex-snapshot
Maximizing the Power of a Local Model for ADMET‐Property Prediction
inferred← citeshttps://doi.org/10.1002/9783527628766.biblioopenalex-snapshot
Bibliography
inferred← citeshttps://doi.org/10.1002/bit.21214openalex-snapshot
MODEL—molecular descriptor lab: A web‐based server for computing structural and physicochemical features of compounds
inferred← citeshttps://doi.org/10.1002/ddr.20044openalex-snapshot
Statistical learning approach for predicting specific pharmacodynamic, pharmacokinetic, or toxicological properties of pharmaceutical agents
inferred← citeshttps://doi.org/10.1002/jcc.21148openalex-snapshot
Prediction of antibacterial compounds by machine learning approaches
inferred← citeshttps://doi.org/10.1002/jps.20985openalex-snapshot
Machine learning approaches for predicting compounds that interact with therapeutic and ADMET related proteins
inferred← citeshttps://doi.org/10.1002/minf.201200086openalex-snapshot
Determination of the Potential of Drug Candidates to Cause Severe Skin Disorders Using Computational Modeling
inferred← citeshttps://doi.org/10.1002/pmic.200500938openalex-snapshot
Recent progresses in the application of machine learning approach for predicting protein functional class independent of sequence similarity
inferred← citeshttps://doi.org/10.1002/qsar.200810015openalex-snapshot
Identifying hERG Potassium Channel Inhibitors by Machine Learning Methods
inferred← citeshttps://doi.org/10.1002/qsar.200810173openalex-snapshot
Optimal Sparse Descriptor Selection for QSAR Using Bayesian Methods
inferred← citeshttps://doi.org/10.1002/qsar.200860090openalex-snapshot
Predicting the Androgenicity of Structurally Diverse Compounds from Molecular Structure Using Different Classifiers
inferred← citeshttps://doi.org/10.1002/qsar.200960021openalex-snapshot
Prediction of HIV‐1 Protease Inhibitors Using Machine Learning Approaches
inferred← citeshttps://doi.org/10.1007/978-0-387-30440-3_134openalex-snapshot
Drug Design with Artificial Neural Networks
inferred← citeshttps://doi.org/10.1007/978-1-0716-4976-3_27openalex-snapshot
Artificial Intelligence and Machine Learning-Based Approaches for Genetic Damage Prediction
inferred← citeshttps://doi.org/10.1007/978-1-4419-0197-2_2openalex-snapshot
(Q)SAR Models for Genotoxicity Assessment
inferred← citeshttps://doi.org/10.1007/978-3-030-19318-8_8openalex-snapshot
Bioinformatics and Medicinal Plant Research: Current Scenario
inferred← citeshttps://doi.org/10.1007/978-3-030-80962-1_121-1openalex-snapshot
Chemical Carcinogens
inferred← citeshttps://doi.org/10.1007/978-3-032-00761-2_121openalex-snapshot
Chemical Carcinogens
inferred← citeshttps://doi.org/10.1007/978-3-540-87559-8_5openalex-snapshot
Efficient Feature Selection for PTR-MS Fingerprinting of Agroindustrial Products
inferred← citeshttps://doi.org/10.1007/978-3-540-92203-2_9openalex-snapshot
Ensemble Modeling for Bio-medical Applications
inferred← citeshttps://doi.org/10.1007/978-981-15-7031-5_2openalex-snapshot
Interdisciplinary Approaches Incorporating Computational Intelligence in Modern Pharmacognosy to Address Biological Problems
inferred← citeshttps://doi.org/10.1007/s10822-011-9431-3openalex-snapshot
Effect of training data size and noise level on support vector machines virtual screening of genotoxic compounds from large compound libraries
inferred← citeshttps://doi.org/10.1007/s12039-023-02208-8openalex-snapshot
Detection of ultrafast solvent dynamics employing a streak camera
inferred← citeshttps://doi.org/10.1007/springerreference_60306openalex-snapshot
Drug Design with Artificial Neural Networks
inferred← citeshttps://doi.org/10.1016/j.chemolab.2006.01.007openalex-snapshot
Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial products
inferred← citeshttps://doi.org/10.1016/j.chemolab.2009.11.002openalex-snapshot
Prediction of the acute toxicity of chemical compounds to the fathead minnow by machine learning approaches
inferred← citeshttps://doi.org/10.1016/j.chemolab.2010.02.001openalex-snapshot
Identification of vasodilators from molecular descriptors by machine learning methods
inferred← citeshttps://doi.org/10.1016/j.chemolab.2012.05.012openalex-snapshot
In silico identification of human pregnane X receptor activators from molecular descriptors by machine learning approaches
inferred← citeshttps://doi.org/10.1016/j.chemolab.2015.07.014openalex-snapshot
Modeling, predicting and virtual screening of selective inhibitors of MMP-3 and MMP-9 over MMP-1 using random forest classification
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2011.08.009openalex-snapshot
A prediction model of substrates and non-substrates of breast cancer resistance protein (BCRP) developed by GA–CG–SVM method
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2013.01.015openalex-snapshot
In silico prediction of spleen tyrosine kinase inhibitors using machine learning approaches and an optimized molecular descriptor subset generated by recursive feature elimination method
inferred← citeshttps://doi.org/10.1016/j.ejps.2008.11.014openalex-snapshot
Support vector machine and pharmacophore-based prediction models of multidrug-resistance protein 2 (MRP2) inhibitors
inferred← citeshttps://doi.org/10.1016/j.eswa.2022.119185openalex-snapshot
Screening model of candidate drugs for breast cancer based on ensemble learning algorithm and molecular descriptor
inferred← citeshttps://doi.org/10.1016/j.jmgm.2005.10.004openalex-snapshot
Quantitative structure–pharmacokinetic relationships for drug clearance by using statistical learning methods
inferred← citeshttps://doi.org/10.1016/j.jmgm.2006.01.007openalex-snapshot
Prediction of estrogen receptor agonists and characterization of associated molecular descriptors by statistical learning methods
inferred← citeshttps://doi.org/10.1016/j.jmgm.2007.03.003openalex-snapshot
Prediction of factor Xa inhibitors by machine learning methods
inferred← citeshttps://doi.org/10.1016/j.jmgm.2007.12.002openalex-snapshot
A support vector machines approach for virtual screening of active compounds of single and multiple mechanisms from large libraries at an improved hit-rate and enrichment factor
inferred← citeshttps://doi.org/10.1016/j.jmgm.2016.05.006openalex-snapshot
Towards cheminformatics-based estimation of drug therapeutic index: Predicting the protective index of anticonvulsants using a new quantitative structure-index relationship approach
inferred← citeshttps://doi.org/10.1016/j.jphotochem.2008.05.020openalex-snapshot
Excited state isomerization and effect of viscosity- and temperature-dependent torsional relaxation on TICT fluorescence of trans-2-[4-(dimethylamino)styryl]benzothiazole
inferred← citeshttps://doi.org/10.1016/j.jrras.2025.101970openalex-snapshot
Intelligent techniques for modelling the effects of methanol leaves extract of Combretum hypopilinum diels (Combretaceae) on hepatic enzymes as indicators of radiation-induced liver injury
inferred← citeshttps://doi.org/10.1016/j.saa.2011.02.005openalex-snapshot
Mechanistic pathway for controlled extraction of guest molecule bound to herring sperm DNA using α-cyclodextrin
inferred← citeshttps://doi.org/10.1016/j.talanta.2015.01.032openalex-snapshot
Prediction of HPLC retention times of tebipenem pivoxyl and its degradation products in solid state by applying adaptive artificial neural network with recursive features elimination
inferred← citeshttps://doi.org/10.1016/j.tiv.2008.09.017openalex-snapshot
In silico prediction of mitochondrial toxicity by using GA-CG-SVM approach
inferred← citeshttps://doi.org/10.1016/j.tiv.2011.05.015openalex-snapshot
Rapid and accurate assessment of seizure liability of drugs by using an optimal support vector machine method
inferred← citeshttps://doi.org/10.1021/acs.jcim.6c00885openalex-snapshot
Reliability-Aware Deep Learning Framework for Chemical Genotoxicity Prediction with Uncertainty Quantification
inferred← citeshttps://doi.org/10.1021/acsestengg.5c00569openalex-snapshot
Integrating Experiments and Machine Learning Modeling to Assess the Half-Wave Potentials of Antibiotics
inferred← citeshttps://doi.org/10.1021/ci400527bopenalex-snapshot
Binary Classification of a Large Collection of Environmental Chemicals from Estrogen Receptor Assays by Quantitative Structure–Activity Relationship and Machine Learning Methods
inferred← citeshttps://doi.org/10.1021/ci800022eopenalex-snapshot
Evaluation of Virtual Screening Performance of Support Vector Machines Trained by Sparsely Distributed Active Compounds
inferred← citeshttps://doi.org/10.1021/jp107775qopenalex-snapshot
Interaction of Twisted Intramolecular Charge-Transfer Probe Loaded Silver Nanoparticles with the Hydrophobic Nanocavities of Cyclodextrins
inferred← citeshttps://doi.org/10.1021/la1048858openalex-snapshot
Orientation of a TICT Probe Trapped in the Peripheral Confined Water Created by Ionic Surfactant Envelope around Silver Nanoparticles
inferred← citeshttps://doi.org/10.1021/tx060136wopenalex-snapshot
Computational Prediction of the Chromosome-Damaging Potential of Chemicals
inferred← citeshttps://doi.org/10.1021/tx300279fopenalex-snapshot
Locally Weighted Learning Methods for Predicting Dose-Dependent Toxicity with Application to the Human Maximum Recommended Daily Dose
inferred← citeshttps://doi.org/10.1039/c7tx00259aopenalex-snapshot
In silico prediction of chemical genotoxicity using machine learning methods and structural alerts
inferred← citeshttps://doi.org/10.1073/pnas.1015024108openalex-snapshot
Quantifying structure and performance diversity for sets of small molecules comprising small-molecule screening collections
inferred← citeshttps://doi.org/10.1080/08927022.2011.616891openalex-snapshot
Identification of DNA adduct formation of small molecules by molecular descriptors and machine learning methods
inferred← citeshttps://doi.org/10.1080/10629360600934168openalex-snapshot
Comparison of methods for chemical-compound affinity prediction
inferred← citeshttps://doi.org/10.1080/10629360902724085openalex-snapshot
Prediction of chemical carcinogenicity by machine learning approaches
inferred← citeshttps://doi.org/10.1093/bib/bbs021openalex-snapshot
Bioinformatics opportunities for identification and study of medicinal plants
inferred← citeshttps://doi.org/10.1093/bioinformatics/btl352openalex-snapshot
Mutagenic probability estimation of chemical compounds by a novel molecular electrophilicity vector and support vector machine
inferred← citeshttps://doi.org/10.1124/mol.106.027623openalex-snapshot
In Silico Prediction of Pregnane X Receptor Activators by Machine Learning Approache
inferred← citeshttps://doi.org/10.1517/17460441.2012.668182openalex-snapshot
Decision tree models for data mining in hit discovery
inferred← citeshttps://doi.org/10.1590/s0001-37652007000400004openalex-snapshot
Chemical carcinogenesis
inferred← citeshttps://doi.org/10.2174/978160805379711201010003openalex-snapshot
QSAR, Complex Networks, Principal Components and Partial Order Analysis of Drug Cardiotoxicity with Proteome Mass- Spectra Topological Indices
inferred← citeshttps://doi.org/10.30895/2312-7821-2023-11-4-390-408openalex-snapshot
In Silico Prediction of Toxicological and Pharmacokinetic Characteristics of Medicinal Compounds
inferred← citeshttps://doi.org/10.3109/10520295.2013.785593openalex-snapshot
Development of a quantitative structure activity relations (QSAR) model to guide the design of fluorescent dyes for detecting amyloid fibrils
inferred← citeshttps://doi.org/10.31788/rjc.2023.1628280openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/ecsoc-11-01371openalex-snapshot
Predicting Proteome-Early Drug Induced Cardiac Toxicity Relationships (Pro-EDICToRs) with Node Overlapping Parameters (NOPs) of a new class of Blood Mass-Spectra graphs
inferred← citeshttps://doi.org/10.4114/ia.v13i44.1043openalex-snapshot
Feature selection on wide multiclass problems using OVA-RFE
inferred← citeshttps://doi.org/10.4137/bbi.s315openalex-snapshot
Prediction of Functional Class of Proteins and Peptides Irrespective of Sequence Homology by Support Vector Machines
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