Fig. 4.

Toxicity prediction by Support Vector Machine algorithm. Support Vector Machine is a popular discriminant analysis algorithm. The first step in this algorithm is to prepare a training data set, such as microarray data for a “carcinogenic compound (positive)” and “non-carcinogenic compound (negative)”. Next, a classifier is developed with the training data using the machine learning algorithm. By using the developed classifier, one can predict a positive / negative outcome (carcinogenic / non-carcinogenic outcome in the figure) for a test compound with an unknown toxicological profile. The accuracy of the prediction by the classifier can be estimated by cross-validation using the training data set. Gray and green indicate ‘Positive’ and ‘Negative’ classification areas, respectively. Red spots indicate the support vectors used for the classification of the test data set.