Hybrid intelligent systems for predictive toxicology - a distributed approach

Abstract

The main objective of this paper is to propose a homogeneous approach to represent and process in silico models for predictive toxicology and also to improve the computational representation of developed models by harmonizing new trends in predictive data mining. We propose to combine local and global models as ensemble experts by mixing technologies in hybrid systems in order to improve the prediction accuracy, and also to provide reasonable training response time by using parallel processing. More investigations have still to be done to develop an optimized strategy, but our approach demonstrates encouraging results.