Abstract: Expression levels of a combination of at least seven genes in a patient sample are measured to separate prostate cancer from normal. Patient samples may be selected from prostate tissue, blood, semen, and urine. A prediction score may be generated based on relative expression levels of the at least seven genes.

Abstract: Identification of a determinative subset of features from within a group of features is performed by training a support vector machine using training samples with class labels to determine a value of each feature, where features are removed based on their the value. One or more features having the smallest values are removed and an updated kernel matrix is generated using the remaining features. The process is repeated until a predetermined number of features remain which are capable of accurately separating the data into different classes. In some embodiments, features are eliminated by a ranking criterion based on a Lagrange multiplier corresponding to each training sample.

Abstract: Expression levels of a combination of at least seven genes in a patient sample are measured to separate prostate cancer from normal. Patient samples may be selected from prostate tissue, blood, semen, and urine. A prediction score may be generated based on relative expression levels of the at least seven genes.

Abstract: Biomarkers are identified by analyzing gene expression data using support vector machines (SVM) to rank genes according to their ability to separate prostate cancer from normal tissue. Expression products of identified genes are detected in patient samples, including prostate tissue, serum, semen and urine, to screen, predict and monitor prostate cancer.