Development of Artificial Intelligence-based In-Silico Toxicity Models. Data Quality Analysis and Model Performance Enhancement through Data Generation.

Development of Artificial Intelligence-based In-Silico Toxicity Models. Data Quality Analysis and Model Performance Enhancement through Data Generation.

Toxic compounds, such as pesticides, are routinely tested against a range of aquatic, avian and mammalian species as part of the registration process. The need for reducing dependence on animal testing has led to an increasing interest in alternative methods such as in silico modelling. The QSAR...

Full description

Bibliographic Details
Main Author: Malazizi, Ladan
Other Authors: Neagu, Daniel
Language:en
Published: University of Bradford 2010
Subjects:
Predictive toxicology
Toxicity data
Pesticides
Artificial intelligence
Data quality
Data generation
Model performance
QSAR
Classification algorithm
Clustering
Imbalanced dataset
Endpoints
Online Access:http://hdl.handle.net/10454/4262

Internet

http://hdl.handle.net/10454/4262

Similar Items