Marco Lorenzini

Learn More
A back-propagation neural network to predict the carcinogenicity of aromatic nitrogen compounds was developed. The inputs were molecular descriptors of different types: electrostatic, topological, quantum-chemical, physicochemical, etc. For the output the index TD50 as introduced by Gold and colleagues was used, giving a continuous numerical parameter(More)
One approach to deal with real complex systems is to use two or more techniques in order to combine their different strengths and overcome each other's weakness to generate hybrid solutions. In this project we pointed out the needs of an improved system in toxicology prediction. An architecture able to satisfy these needs has been developed. The main tools(More)
Until recently, problem solvers have typically used single-technique-based tools to build the solution. Also in the field of predictive toxicology, a few systems have been developed in that way, with positive preliminary results. One approach to deal with real complex systems is to use two or more techniques in order to combine their different strenghts and(More)
  • 1