Bayesian Classifiers for Chemical Toxicity Prediction

Abstract

A major concern across the globe is the growing number of new chemicals that are brought to use on a regular basis without having any knowledge about their toxic behavior. The challenge here is that the growth in the number of chemicals is fast, and the traditional standards for toxicity testing involve a slow and expensive process of in vivo animal testing. Hence, a number of attempts are being made to find alternate methods of toxicity testing. In this paper we explore Bayesian classifiers and show that if we approximate posterior in the Bayesian classifier with specially crafted basis functions, we can improve upon the performance. We have tested our methods using data sets from the Environmental Protection Agency (EPA). Our experimental study demonstrated the utility of the advanced Bayesian classification approach.