Comparative classification study of toxicity mechanisms using support vector machines and radial basis function neural networks
- 11 April 2005
- journal article
- Published by Elsevier BV in Analytica Chimica Acta
- Vol. 535 (1-2), 259-273
- https://doi.org/10.1016/j.aca.2004.11.066
Abstract
No abstract availableKeywords
This publication has 21 references indexed in Scilit:
- Diagnosing Breast Cancer Based on Support Vector MachinesJournal of Chemical Information and Computer Sciences, 2003
- The Present Utility and Future Potential for Medicinal Chemistry of QSAR / QSPR with Whole Molecule DescriptorsCurrent Topics in Medicinal Chemistry, 2002
- Comparative assessment of methods to develop QSARs for the prediction of the toxicity of phenols to Tetrahymena pyriformisChemosphere, 2002
- Multivariate Discrimination between Modes of Toxic Action of PhenolsQuantitative Structure-Activity Relationships, 2002
- Predicting three narcosis mechanisms of aquatic toxicityToxicology Letters, 2002
- Identifying the mechanism of aquatic toxicity of selected compounds by hydrophobicity and electrophilicity descriptorsToxicology Letters, 2002
- Gene Selection for Cancer Classification using Support Vector MachinesMachine Learning, 2002
- Multiclass cancer diagnosis using tumor gene expression signaturesProceedings of the National Academy of Sciences of the United States of America, 2001
- Support vector machine classification and validation of cancer tissue samples using microarray expression dataBioinformatics, 2000
- Local modelling with radial basis function networksChemometrics and Intelligent Laboratory Systems, 2000