Alternative feature mapping for heterogeneous gene data classification

In order to overcome the limitation on small sizes of gene datasets, many meta-classification methods which ensemble classifiers with different datasets have been developed. However, due to discrepancies of the characteristics within heterogeneous or cross-platform datasets, the number of common and significant genes is usually small. Instead of matching common genes between heterogeneous datasets, we propose a novel solution, alternative feature mapping approach (AFM), to utilize related and discriminative gene expressions while not necessarily having exact matches. Genes in the training dataset are clustered and mapped to the test dataset as gene groups. Through analyzing the correlation within gene groups between training and test datasets, related significant genes can be applied for classification. We conducted experiments consisting of 8 heterogeneous datasets with different cancer types and platforms to test the effectiveness of AFM. Our experiments show that classification performance is greatly improved using suitable significant genes selected by AFM.