A Latent Space Support Vector Machine (LSSVM) Model for Cancer Prognosis

Gene expression microarray analysis is a rapid, low cost method of analyzing gene expression profiles for cancer prognosis/diagnosis. Microarray data generated from oncological studies typically contain thousands of expression values with few cases. Traditional regression and classification methods require first reducing the number of dimensions via statistical or heuristic methods. Partial Least Squares (PLS) is a dimensionality reduction method that builds a least squares regression model in a reduced dimensional space. It is well known that Support Vector Machines (SVM) outperform least squares regression models. In this study, we replace the PLS least squares model with a SVM model in the PLS reduced dimensional space. To verify our method, we build upon our previous work with a publicly available data set from the Gene Expression Omnibus database containing gene expression levels, clinical data, and survival times for patients with non-small cell lung carcinoma. Using 5-fold cross validation, and Receiver Operating Characteristic (ROC) analysis, we show a comparison of classifier performance between the traditional PLS model and the PLS/SVM hybrid. Our results show that replacing least squares regression with SVM, we increase the quality of the model as measured by the area under the ROC curve