Identification of the Gene Signatures in Microarray Data by CID

For the topic of "identification of gene signatures in microarray data," statisticians have proposed lots of methods to accurately select the genes which are most representative. According to the results of previous researches, feature selection is essential in accurately classifying objects into classes. Therefore, we propose to use the coefficient of intrinsic dependence (CID) in identifying signatures. From the simulation results, we find that CID has a proper and stable detecting power in location or scale difference and under the different assumptions of distribution.he CID is also exercised on a breast cancer microarray data. We find that the selected genes by subCID, a expansion of CID, are thought more accurate and powerful in class estimation than the conventional statistics.ccording to the results of our study, there is convincing evidence that CID and subCID are more accurate and powerful in feature selection, and the selected genes are well-performed in classification studies, such as class estimation.