The classification of cancer stage microarray data

Correctly diagnosing the cancer stage is most important for selecting an appropriate cancer treatment option for a patient. Recent advances in microarray technology allow the cancer stage to be predicted using gene expression patterns. The cancer stage is in ordinal scale. In this paper, we employ strict ordinal regressions including cumulative logit model in traditional statistics with data dimensionality reduction, and distribution free approaches of large margin rank boundaries implemented by the support vector machine, as well as an ensemble ranking scheme to model the cancer stage using gene expression microarray data. Predictive genes included in models are selected by univariate feature ranking, and recursive feature elimination. We perform cross-validation experiments to assess and compare classification accuracies of ordinal and non-ordinal algorithms on five cancer stage microarray datasets. We conclude that a strict ordinal classifier trained by a validated approach can predict the cancer stage more accurately than traditional non-ordinal classifiers without considering the order of cancer stages.