Semi-supervised classification using sparse representation for cancer recurrence prediction

Abstract

Gene expression profiles have been used to predict cancer recurrence or other clinical outcomes of cancer patients. However, clinical information of cancer patients is often incomplete, which yields many unlabeled samples that cannot be used in supervised learning. In this is paper, we develop a novel semi-supervised leaning (SSL) method that uses both labeled and unlabeled patient samples to predict cancer recurrence. Our new SSL algorithm employs a sparse representation approach where a labeled sample is represented as a combination of a small number of properly chosen unlabeled samples. Experiments with a set of gene expression data from patients with colorectal cancer(CRC) demonstrate that our SSL algorithm can improve prediction accuracy compared to other two SSL methods including TSVM and T3VM, and the traditional support vector machine.