Biomarker Identification for Prostate Cancer Using an Efficient Feature Selection Algorithm

In recent years, there has been an increased interest in using protein mass spectrometry to identify biomarkers that discriminate diseased from healthy individuals. A biomarker is a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathological processes, or pharmacological responses to a therapeutic intervention. Identifying biomarkers will be an important step towards disease characterization and patient management. One challenge of biomarker identification is how to handle the high dimensional mass spectral data. In this thesis, we applied an efficient feature selection algorithm to mass spectrometry data obtained from prostate tissue samples to identify prostate specific cancer biomarkers. Experiments showed that the proposed method achieved high sensitivities and specificities and outperformed many other currently used feature selection algorithms.