6533b7d7fe1ef96bd1269127
RESEARCH PRODUCT
Predictability and prediction of lowest observed adverse effect levels in a structurally heterogeneous set of chemicals
Jorge GalvezJ.v. De Julián-ortizRamón García-domenechLionello Poglianisubject
Multilinear mapComputer scienceLinear modelReproducibility of ResultsContrast (statistics)BioengineeringGeneral MedicineModels TheoreticalLinear discriminant analysiscomputer.software_genreRegressionLowest-observed-adverse-effect levelSet (abstract data type)Structure-Activity RelationshipDrug DiscoveryStatisticsLinear ModelsAnimalsMolecular MedicineData miningOrganic ChemicalsPredictabilityToxicity Tests Chroniccomputerdescription
A database of chronic lowest observed adverse effect levels (LOAELs) for 234 compounds, previously compiled from different sources (Toxicology Letters79, 131-143 (1995)), was modelled using graph theoretical descriptors. This study reveals that data are not homogeneous. Only those data originating from the U.S. Environmental Protection Agency (EPA) reports could be well modelled by multilinear regression (MLR) and linear discriminant analysis (LDA). In contrast, data available from the specific procedures of the National Toxicology Program (NTP) database introduced noise and did not render good models either alone, or in combination with the EPA data.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2005-04-05 | SAR and QSAR in Environmental Research |