Functionally relevant prediction model for colorectal cancer

Filtered selection coupled with support vector machines generate functionally relevant prediction model for colorectal cancer. In this study, we built a model that uses Support Vector Machine (SVM) to classify cancer and normal samples using Affymetrix exon microarray data obtained from 90 samples of 48 patients diagnosed with CRC. From the 22,011 genes, we selected the 20, 30, 50, 100, 200, 300 and 500 genes most relevant to CRC using the Minimum-Redundancy–Maximum-Relevance (mRMR) technique. With these gene sets, an SVM model was designed using four different kernel types (linear, polynomial, radial basis function and sigmoid). We conducted a pair-wise comparison of Tumor vs Normal samples obtained from cancer patients. Array data was processed using Expression Console Patients detail for sample 052311 and 082812 are missing.