Development of a Prediction Model for Mutagenicity - Validation of Ames Test Data

Abstract

We are developing a classification model for predicting mutagenicity of diverse organic compounds. We have proposed an ensemble model, in which many support vector machine models are constructed and integrated to predict mutagenicity. This model successfully predicted mutagenicity with accuracy rate of 79.6 %. However, on the other hand, the results of prediction suggested that some wrong data were included in database. Therefore, in this study, Ames test was carried out for some suspicious compounds. First, an ensemble model was constructed using the dataset that was assembled by Hansen et al. Then Ames test was carried out for five suspicious compounds that were registered in the database as negative. As a result, three of five compounds were judged as positive. This suggests that the database include some wrong data and our model can find these compounds efficiently.