Various classification methods have been used topredict the class of tissue samples based on gene expressiondata. Prediction Analysis for Microarrays (PAM) is one of the topclassifiers that has been extensively used for cancer classification.In this paper a novel method of combining expression data fromgene pairs is used to improve the overall accuracy of PAM.Recent studies suggest that deregulation of pathways, ratherthan individual genes, may be critical in triggering carcinogenesis.The pathway deregulation is often caused by the simultaneousderegulation of more than one genes in the pathway. Robust genepair combinations may exploit these underlying bio-molecularreactions to provide better biomarkers for cancer, as comparedto single genes. In this work, we used gene pair combinations,called doublets, to improve the accuracy of PAM. We validatedthe proposed approach with nine cancer datasets. The accuracyof PAM, using these doublets, increased consistently across thesedatasets, in some cases with a significant margin (13%).
['Pankaj Chopra', 'Jaewoo Kang', 'Jinseung Lee']
Using Gene Pair Combinations to Improve the Accuracy of the PAM Classifier