Tumor Classification Based on Partial Least Square Regression

As the gene expression profiling data being with the characteristic of severe multicollinearity, small samples, and high dimension, it is difficult to build tumor classification model. Partial least square regression was applied as dimension reduction method to the model of tumor classification. Respectively, principal components are extracted from five gene expression profiling data sets: Gastric, C.vs.SC, Colon, Lung and Acute Leukemia. Then, the extracted principal components are used to classify the samples combining with SVM method. The results showed that the partial least square regression combining with SVM can be used not only in two-class problem, but also in multiclass problem reliably.